UF Interventions Meta-analysis

UF Interventions Meta-analysis-theirs.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Uterine fibroids follow-up treatment meta-analysis\n\nOur goal is to estimate the probabilities of requiring one of a suite of candidate follow-up treatments following randomization to a given initial treatment for uterine fibroids. Specifically, we are interested in estimating:\n\n$$Pr(I_2|I_1 =i,T=t)$$\n\nwhere $I_1$ is an initial intervention, which take specific values $i = 1, 2, \\ldots , K$ for each of $K$ candidate intervention types, $I_2$ is the followup intervention that also may take any of the same values of $i$, and $T$ is followup time in months, which will generally be either 6 or 12 months.\n\nOur current set of candidate interventions include:\n\n- Myomectomy\n- Hysterectomy\n- Ablation\n- UAE\n- Magnetic resonance imaging-guided high-intensity focused ultrasound (MRIgFUS) \n- Ablation +/- hysteroscopic myomectomy\n- No intervention\n\nRather than model each conditional probability independently, we will instead model the outcomes for a treatment arm as a multinomial random variable. That is,\n\n$$\\{X_{I_2} \\} ∼ \\text{Multinomial}(N_{I_1}=i, \\{\\pi_i\\})$$\n\nwhere $\\{X_{I_2}\\}$ is the vector of outcomes corresponding to each of the possible followup interventions listed above, $N_{I_1}=i$ is the number of women randomized to the initial intervention i, and $\\{\\pi_i\\}$ is a vector of conditional transition probabilities corresponding to $Pr(I_2|I_1 = i, T = t)$, as specified above. The multinomial distribution is a multivariate generalization of the categorical distribution, which is what the above simplifies to when modeling the outcome for a single patient. The multivariate formulation allows us to model study-arm-specific outcomes, incorporating covariates that are specific to that arm or study.\n       \nThe quantities of interest are the vectors of transition probabilities $\\{\\pi_i\\}$ corresponding to each of the initial candidate interventions. A naive approach to modeling these is to assign a vague Dirichlet prior distribution to each set, and perform Bayesian inference using the multinomial likelihood, with which the Dirichlet is conjugate, to yield posterior estimates for each probability. However, there may be additional information with which to model these probabilities, which may include:\n\n- followup time for each study\n- arm-specific demographic covariates (e.g. race, mean age) \n- study-specific random effects\n\nhence, a given transition probability $\\pi_{ijk}$ – the probability of transitioning from initial intervention $i$ to followup intervention $j$ in study $k$ – may be modeled as:\n\n$$\\text{logit}(\\pi_{ijk})= \\theta_{ij} + X_k \\beta_{ij} + \\epsilon_k$$\n\nwhere $\\theta_{ij}$ is a baseline transition probability (on the logit scale), $X_k$ a matrix of study(-arm)-specific covariates, $\\beta_{ij}$ the corresponding coefficients, and $\\epsilon_k$ a mean-zero random effect for study k. We will initially consider (1) follow-up time and (2) mean/median age as covariates.\n \nAn attractive benefit to using Bayesian inference to estimate this model is that it is easy to generate predictions from the model, via the posterior predictive distribution. For example, we could estimate the distribution of the expected proportion of women requiring a particular followup intervention; this estimate would factor in both the residual uncertainty in the transition probability estimates, as well as the sampling uncertainty of the intervention."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport pymc3 as pm\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pdb\nsns.set(style=\"ticks\")",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Data Preparation\n\nImport data from worksheets in Excel spreadsheet."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_file = 'UF Subsequent Interventions Data_Master_updated.xlsx'",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing = ['NA', 'NR', 'ND', '?', 'null']\n\nmisc_data = pd.read_excel('data/' + data_file, sheetname='MISC (SP)', na_values=missing)\nmisc_data = misc_data[~misc_data['baseline_n'].isnull()].drop('notes', axis=1)\nrows, cols = misc_data.shape\nprint('Occlusion rows={0}, columns={1}, missing={2}'.format(rows, cols,\n                                                        misc_data.isnull().sum().sum()))\n\nmed_vs_iac_data = pd.read_excel('data/' + data_file, sheetname='Med vs IAC JW', na_values=missing)\nmed_vs_iac_data = med_vs_iac_data[~med_vs_iac_data['trial_arm'].isnull()].drop('notes', axis=1)\nrows, cols = med_vs_iac_data.shape\nprint('Med vs IAC rows={0}, columns={1}, missing={2}'.format(rows, cols, \n                                                            med_vs_iac_data.isnull().sum().sum()))\n\nmed_vs_med_data = pd.read_excel('data/' + data_file, sheetname='Med vs Med DVE', na_values=missing)\nmed_vs_med_data = med_vs_med_data[~med_vs_med_data['baseline_n'].isnull()].drop('notes', axis=1)\nrows, cols = med_vs_med_data.shape\nprint('Med vs Med rows={0}, columns={1}, missing={2}'.format(rows, cols, \n                                                            med_vs_med_data.isnull().sum().sum()))\n\nuae_data = pd.read_excel('data/' + data_file, sheetname='UAE SK')\nuae_data = uae_data[~uae_data['baseline_n'].isnull()].drop('notes', axis=1)\nrows, cols = uae_data.shape\nprint('UAE rows={0}, columns={1}, missing={2}'.format(rows, cols, \n                                                            uae_data.isnull().sum().sum()))\n\ndatasets = [misc_data, med_vs_iac_data, med_vs_med_data, uae_data]",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Occlusion rows=31, columns=13, missing=6\nMed vs IAC rows=49, columns=13, missing=46\nMed vs Med rows=67, columns=13, missing=13\nUAE rows=32, columns=13, missing=0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "unique_inerventions = set(np.concatenate([d.intervention.values for d in datasets]))",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use the following lookup table to create \"intervention category\" field in each dataset."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# %load intervention_lookup.py\nintervention_lookup = {'Ablation': 'ablation',\n 'Ablation+/- hysteroscopic myomectomy': 'ablation',\n 'Asoprisnil 10 mg': 'med_manage',\n 'Asoprisnil 25 mg': 'med_manage',\n 'Asoprisnil 5 mg': 'med_manage',\n 'Ulipristal (CD20)': 'med_manage',\n 'Ulipristal (CDB10)': 'med_manage',\n 'Hysterectomy': 'hysterectomy',\n 'LBCUV': 'uae',\n 'LP + GnRH agonist plus raloxifene': 'med_manage',\n 'LP + placebo': 'med_manage',\n 'LPA+ MPA / LPA+placebo': 'med_manage',\n 'LPA+ placebo / LPA+MPA': 'med_manage',\n 'LUNA plus LBCUV': 'ablation',\n 'Myomectomy': 'myomectomy',\n 'No treatment': 'control',\n 'No treatment (control)': 'control',\n 'Placebo': 'control',\n 'Raloxifene, 180mg/day': 'med_manage',\n 'SC implant of 3.6 goserelin + placebo (3 months) then tibolone 2.5 mg daily (3 months)': 'med_manage',\n 'SC implant of 3.6 goserelin + placebo (6 months)': 'med_manage',\n 'SC implant of 3.6 goserelin + tibolone 2.5 mg daily (6 months)': 'med_manage',\n 'Surgery': 'DROP',\n 'Tibolone': 'med_manage',\n 'UAE': 'uae',\n 'UAE only': 'uae',\n 'UAE plus goserelin acetate depot': 'uae',\n 'buserelin + goserelin': 'med_manage',\n 'buserelin, intranasal': 'med_manage',\n 'cabergoline': 'med_manage',\n 'diphereline': 'med_manage',\n 'gestrinone, 2.5mg': 'med_manage',\n 'gestrinone, 2.5mg oral + gestrinone, 5mg oral + gestrinone, 5mg vaginal': 'med_manage',\n 'gestrinone, 5mg': 'med_manage',\n 'gestrinone, 5mg vaginal': 'med_manage',\n 'goserelin, subcutaneous': 'med_manage',\n 'healthy controls': 'control',\n 'hormone replacement therapy, transdermal': 'DROP',\n 'hysterectomy or myomectomy': 'DROP',\n 'letrozole, 2.5mg': 'med_manage',\n 'leuprolide': 'med_manage',\n 'leuprolide acetate depot (11.25 mg q 3 months) + Placebo': 'med_manage',\n 'leuprolide acetate depot (11.25 mg q 3 months) + tibolone 2.5 mg/d orally': 'med_manage',\n 'leuprolide acetate depot (3.75 mg/28 d) + placebo (B)': 'med_manage',\n 'leuprolide plus (tibolone 2.5 mg daily) (A)': 'med_manage',\n 'leuprolide plus MPA': 'med_manage',\n 'leuprolide plus estrogen-progestin': 'med_manage',\n 'leuprolide plus placebo': 'med_manage',\n 'leuprolide plus progestin': 'med_manage',\n 'leuprolide plus raloxifene 60 mg daily': 'med_manage',\n 'leuprolide, 1.88mg': 'med_manage',\n 'leuprolide, 3.75mg': 'med_manage',\n 'mifepristone, 10mg': 'med_manage',\n 'mifepristone, 10mg + mifepristone, 5mg': 'med_manage',\n 'mifepristone, 2.5mg': 'med_manage',\n 'mifepristone, 5mg': 'med_manage',\n 'placebo': 'control',\n 'raloxifene 180 mg daily': 'med_manage',\n 'raloxifene 60 mg daily': 'med_manage',\n 'tamoxifen 20 mg daily': 'med_manage',\n 'tibolone': 'med_manage',\n 'tibolone, 2.5mg': 'med_manage',\n 'transdermal estrogen replacement therapy': 'med_manage',\n 'triptorelin, 100ug': 'med_manage',\n 'triptorelin, 100ug + triptorelin, 20ug + triptorelin, 5ug': 'med_manage',\n 'triptorelin, 20ug': 'med_manage',\n 'triptorelin, 3.6mg/mo': 'med_manage',\n 'triptorelin, 5ug': 'med_manage',\n 'ulipristal acetate followed by placebo': 'med_manage',\n 'ulipristal acetate followed by progestin': 'med_manage',\n 'ulipristal, 10mg': 'med_manage',\n 'ulipristal, 5mg': 'med_manage',\n 'HIFU': 'MRgFUS',\n 'HIFU with CEUS': 'MRgFUS',\n 'LUAO': 'uae',\n 'UAE plus PVA': 'uae',\n 'UAE plus TAG': 'uae',\n 'UAE with PVA': 'uae',\n 'UAE with PVA particles, large': 'uae',\n 'UAE with PVA particles, small': 'uae',\n 'UAE with SPA': 'uae',\n 'UAE with SPVA': 'uae',\n 'UAE with TAG': 'uae',\n 'UAE with TAG microspheres': 'uae',\n 'myomectomy': 'myomectomy',\n 'myomectomy with vasopressin': 'myomectomy',\n 'myomectomy, abdominal': 'myomectomy',\n 'myomectomy, laparoscopic': 'myomectomy',\n 'myomectomy, loop ligation with vasopressin': 'myomectomy',\n 'myomectomy, minilaparotomic': 'myomectomy'}\n",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Assign intervention **categories** to each arm"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "datasets = [d.assign(intervention_cat=d.intervention.replace(intervention_lookup)) for d in datasets]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "intervention_categories = set(intervention_lookup.values())\nintervention_categories",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "{'DROP',\n 'MRgFUS',\n 'ablation',\n 'control',\n 'hysterectomy',\n 'med_manage',\n 'myomectomy',\n 'uae'}"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Import demographic information"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "demographics = pd.read_excel('data/' + data_file, sheetname='ALL_DEMO_DATA', na_values=missing)\ndemographics.columns",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['study_id', 'Citation', 'FamCode', 'FamDesig', 'NCT', 'ArmsN',\n       'ArmCategory', 'Group_Desc', 'New Grouping', 'Demo_Category',\n       'Demo_specify', 'BL N', 'Denom_N', 'BL %', 'BL Mean', 'BL SD', 'BL_SE',\n       'BL_Median', 'BL Min', 'BL Max', 'BL 95% L', 'BL 95% H',\n       'BL_group_diff', 'Comments'],\n      dtype='object')"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Extract columns of interest"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_data = demographics.loc[demographics.Demo_Category=='Age', ['study_id', 'New Grouping', 'BL Mean', 'BL SD']]",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Clean arm labels"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_data = age_data.assign(arm=age_data['New Grouping'].str.replace(':','')).drop('New Grouping', axis=1)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "age_data.arm.unique()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['G2', 'G1', 'G1b', 'G1a', 'G3', 'CG', 'G1c', 'G1+G2', 'G1a+G1b+G1c'], dtype=object)"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Concatenate all datasets"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data = pd.concat(datasets)",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Clean up study arm field"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_arm = all_data.trial_arm.str.replace(':','').str.replace(' ', '').str.replace('Group', 'G')\nall_data = all_data.assign(arm=all_arm).drop('trial_arm', axis=1)",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data.arm.unique()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['G1', 'G2', 'G3', 'CG', 'G1a', 'G1b', 'G1c', 'CG1', 'CG2', 'G1/CG',\n       'CG/G1', 'G1a+G1b', 'G1a+G1b+G1c', 'G1+G2'], dtype=object)"
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Clean up study ID field. Currently contains non-numeric entries. Will strip out the first study ID from the compund labels, as this is the parent study ID."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data.study_id.unique()",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([23, 347, 1400, 1529, 1806, 1889, 2375, 2967, 3382, 3690, 3785, 5186,\n       5474, 414, 1849, 3016, 3181, 3324, 3674, 4258, 4468, 4858, 4960,\n       5276, 5302, 6091, 6263, 6696, 7155, 7504, 7797, 7936, 95.0, 629.0,\n       757.0, 1290.0, 2318.0, 2555.0, 2635.0, 3312.0, 3978.0, 4787.0,\n       4961.0, 5721.0, 6393.0, 6903.0, 7139.0, 7309.0, 7530.0, 7589.0,\n       7763.0, '3803_3052', 1546, '3365_2026_1657_986',\n       '3819_815_1986_2759_2971_\\n3120_3175_3192_3678_3721', 4789, 2006], dtype=object)"
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "str_mask = all_data.study_id.str.isnumeric()==False\nall_data.loc[str_mask, 'study_id'] = all_data.study_id[str_mask].apply(lambda x: x[:x.find('_')])\nall_data.study_id = all_data.study_id.astype(int)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data.study_id.unique()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([  23,  347, 1400, 1529, 1806, 1889, 2375, 2967, 3382, 3690, 3785,\n       5186, 5474,  414, 1849, 3016, 3181, 3324, 3674, 4258, 4468, 4858,\n       4960, 5276, 5302, 6091, 6263, 6696, 7155, 7504, 7797, 7936,   95,\n        629,  757, 1290, 2318, 2555, 2635, 3312, 3978, 4787, 4961, 5721,\n       6393, 6903, 7139, 7309, 7530, 7589, 7763, 3803, 1546, 3365, 3819,\n       4789, 2006])"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Here is what the data look like after merging."
    },
    {
      "metadata": {
        "scrolled": true,
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data.head()",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "   study_id    intervention baseline_n followup_interval  followup_n  \\\n0        23  HIFU with CEUS         17                12        17.0   \n1        23            HIFU         16                12        16.0   \n2       347   UAE with SPVA         30                12        27.0   \n3       347    UAE with TAG         30                12        29.0   \n4      1400             UAE         63                 6        62.0   \n\n   hysterectomy  myomectomy  uae  MRIgFUS  ablation  iud  no_treatment  \\\n0           0.0         0.0  0.0      1.0       0.0  0.0          16.0   \n1           0.0         0.0  0.0      3.0       0.0  0.0          13.0   \n2           1.0         0.0  0.0      0.0       0.0  0.0          26.0   \n3           0.0         0.0  0.0      0.0       0.0  0.0          29.0   \n4           0.0         1.0  5.0      0.0       0.0  0.0          56.0   \n\n  intervention_cat arm  \n0           MRgFUS  G1  \n1           MRgFUS  G2  \n2              uae  G1  \n3              uae  G2  \n4              uae  G1  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>study_id</th>\n      <th>intervention</th>\n      <th>baseline_n</th>\n      <th>followup_interval</th>\n      <th>followup_n</th>\n      <th>hysterectomy</th>\n      <th>myomectomy</th>\n      <th>uae</th>\n      <th>MRIgFUS</th>\n      <th>ablation</th>\n      <th>iud</th>\n      <th>no_treatment</th>\n      <th>intervention_cat</th>\n      <th>arm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>23</td>\n      <td>HIFU with CEUS</td>\n      <td>17</td>\n      <td>12</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>16.0</td>\n      <td>MRgFUS</td>\n      <td>G1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>23</td>\n      <td>HIFU</td>\n      <td>16</td>\n      <td>12</td>\n      <td>16.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>MRgFUS</td>\n      <td>G2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>347</td>\n      <td>UAE with SPVA</td>\n      <td>30</td>\n      <td>12</td>\n      <td>27.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>uae</td>\n      <td>G1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>347</td>\n      <td>UAE with TAG</td>\n      <td>30</td>\n      <td>12</td>\n      <td>29.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>29.0</td>\n      <td>uae</td>\n      <td>G2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1400</td>\n      <td>UAE</td>\n      <td>63</td>\n      <td>6</td>\n      <td>62.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>56.0</td>\n      <td>uae</td>\n      <td>G1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data.groupby('intervention_cat')['study_id'].count()",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "intervention_cat\nDROP              8\nMRgFUS            2\nablation          3\ncontrol          11\nhysterectomy      7\nmed_manage      100\nmyomectomy       14\nuae              34\nName: study_id, dtype: int64"
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Merge age data with outcomes"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data_merged = pd.merge(all_data, age_data, on=['study_id', 'arm'])",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For now, drop arms with no reported followup time (we may want to impute these):"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "all_data_merged = all_data_merged.dropna(subset=['followup_interval'])",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Parse followup intervals that are ranges, creating `fup_min` and `fup_max` fields."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset = all_data_merged.assign(fup_min=0, fup_max=all_data.followup_interval.convert_objects(convert_numeric=True).max()+1)\nrange_index = dataset.followup_interval.str.contains('to').notnull()\nrange_vals = dataset[range_index].followup_interval.apply(lambda x: x.split(' '))\ndataset.loc[range_index, ['fup_min']] = range_vals.apply(lambda x: float(x[0]))\ndataset.loc[range_index, ['fup_max']] = range_vals.apply(lambda x: float(x[-1]))\ndataset.loc[range_index, ['followup_interval']] = np.nan\ndataset['followup_interval'] = dataset.followup_interval.astype(float)",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Fill missing values"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset.loc[dataset.followup_n.isnull(), 'followup_n'] = dataset.loc[dataset.followup_n.isnull(), 'baseline_n']",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset.loc[dataset.no_treatment.isnull(), 'no_treatment'] = dataset.followup_n - dataset[[ 'hysterectomy', 'myomectomy', 'uae',\n                                                        'MRIgFUS', 'ablation', 'iud']].sum(1)[dataset.no_treatment.isnull()]",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset.followup_interval.unique()",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 12. ,   6. ,   nan,  24. ,   2. ,   1. ,   3. ,   5.5,   9. ,\n        18. ,   0. ,   7. ,  60. ])"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset['BL Mean'].unique()",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([43.1, 42, 43.9, 41.7, 41, 43.5, 40.3, 42.7, 45, 44, 38.26, 32.1,\n       34.3, 44.9, 42.5, 43.3, 38.4, 37.5, 45.9, 44.5, 33.97, 34, 41.3,\n       42.9, 42.1, 43.4, 37.7, 43, 40.2, 41.1, 49.1, 48.6, 36.3, 35.9,\n       37.2, 54.2, 51.2, 43.6, nan, 38.9, 37.1, 41.4, 36.9, 41.6, 39, 39.6,\n       39.67, 36.87, 30.94, 31, 39.5, 42.8, 56.2, 57.9, 50.2, 50.6, 34.4,\n       42.2, 49.2, 32.6, 48.4, 33.8, 38.1, 37, 32.3, 43.2, 44.6, 45.4,\n       46.4, 48.5, 48.3], dtype=object)"
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Identify crossover studies"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "crossover_studies = 7155, 3324, 414, 95, 7139, 6903, 3721, 3181, 4858, 4960, 4258, 4789, 2006, 2318",
      "execution_count": 27,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset['crossover_study'] = dataset.study_id.isin(crossover_studies)",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset.head()",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "   study_id    intervention baseline_n  followup_interval  followup_n  \\\n0        23  HIFU with CEUS         17               12.0        17.0   \n1        23            HIFU         16               12.0        16.0   \n2       347   UAE with SPVA         30               12.0        27.0   \n3       347    UAE with TAG         30               12.0        29.0   \n4      1400             UAE         63                6.0        62.0   \n\n   hysterectomy  myomectomy  uae  MRIgFUS  ablation  iud  no_treatment  \\\n0           0.0         0.0  0.0      1.0       0.0  0.0          16.0   \n1           0.0         0.0  0.0      3.0       0.0  0.0          13.0   \n2           1.0         0.0  0.0      0.0       0.0  0.0          26.0   \n3           0.0         0.0  0.0      0.0       0.0  0.0          29.0   \n4           0.0         1.0  5.0      0.0       0.0  0.0          56.0   \n\n  intervention_cat arm BL Mean  BL SD  fup_max  fup_min crossover_study  \n0           MRgFUS  G1    43.1    5.3     61.0        0           False  \n1           MRgFUS  G2      42    5.4     61.0        0           False  \n2              uae  G1    43.9    5.0     61.0        0           False  \n3              uae  G2    41.7    5.4     61.0        0           False  \n4              uae  G1      41    3.5     61.0        0           False  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>study_id</th>\n      <th>intervention</th>\n      <th>baseline_n</th>\n      <th>followup_interval</th>\n      <th>followup_n</th>\n      <th>hysterectomy</th>\n      <th>myomectomy</th>\n      <th>uae</th>\n      <th>MRIgFUS</th>\n      <th>ablation</th>\n      <th>iud</th>\n      <th>no_treatment</th>\n      <th>intervention_cat</th>\n      <th>arm</th>\n      <th>BL Mean</th>\n      <th>BL SD</th>\n      <th>fup_max</th>\n      <th>fup_min</th>\n      <th>crossover_study</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>23</td>\n      <td>HIFU with CEUS</td>\n      <td>17</td>\n      <td>12.0</td>\n      <td>17.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>16.0</td>\n      <td>MRgFUS</td>\n      <td>G1</td>\n      <td>43.1</td>\n      <td>5.3</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>23</td>\n      <td>HIFU</td>\n      <td>16</td>\n      <td>12.0</td>\n      <td>16.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>MRgFUS</td>\n      <td>G2</td>\n      <td>42</td>\n      <td>5.4</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>347</td>\n      <td>UAE with SPVA</td>\n      <td>30</td>\n      <td>12.0</td>\n      <td>27.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>uae</td>\n      <td>G1</td>\n      <td>43.9</td>\n      <td>5.0</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>347</td>\n      <td>UAE with TAG</td>\n      <td>30</td>\n      <td>12.0</td>\n      <td>29.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>29.0</td>\n      <td>uae</td>\n      <td>G2</td>\n      <td>41.7</td>\n      <td>5.4</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1400</td>\n      <td>UAE</td>\n      <td>63</td>\n      <td>6.0</td>\n      <td>62.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>56.0</td>\n      <td>uae</td>\n      <td>G1</td>\n      <td>41</td>\n      <td>3.5</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Export data for posterity"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "dataset.to_csv('data/UF_interventions_clean.csv', na_rep=None)",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "outcome_cats = [ 'hysterectomy', 'myomectomy', 'uae',\n       'MRIgFUS', 'ablation', 'iud', 'no_treatment']",
      "execution_count": 31,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "studies = dataset.study_id.unique()\nstudy_index = np.array([np.argwhere(studies==i).squeeze() for i in dataset.study_id])",
      "execution_count": 32,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model Specification"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def append(tensor, value):\n    return T.concatenate([tensor, T.stack([value], axis=0)])",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import theano.tensor as T\nfrom numpy.ma import masked_values\n\n\nSumTo1 = pm.transforms.SumTo1()\ninverse_logit = pm.transforms.inverse_logit\n\ndef specify_model(model, intervention):\n    \n    intervention_data = dataset[(dataset.intervention_cat==intervention)\n                            & ~dataset[outcome_cats].isnull().sum(axis=1).astype(bool)].copy()\n    \n    intervention_data.loc[intervention_data['followup_interval'].isnull(), 'followup_interval'] = 17.33\n    followup_masked = masked_values(intervention_data.followup_interval.values, 17.33)\n    followup_min, followup_max = intervention_data[['fup_min', 'fup_max']].values.T\n\n    outcomes = intervention_data[[ 'hysterectomy', 'myomectomy', 'uae',\n           'MRIgFUS', 'ablation', 'iud', 'no_treatment']].values\n    \n    if np.isnan(outcomes).any():\n        print('Missing values in outcomes for', intervention)\n\n    followup_n = intervention_data.followup_n.values\n\n    # Center age at 40\n    intervention_data.loc[intervention_data['BL Mean'].isnull(), 'BL Mean'] = 90\n    age_masked = masked_values(intervention_data['BL Mean'].values - 40, 50)\n    \n    studies = intervention_data.study_id.unique()\n    study_index = np.array([np.argwhere(studies==i).squeeze() for i in intervention_data.study_id])\n\n    study_id = intervention_data.study_id.values\n    \n    n_studies = len(set(study_id))\n\n    n_outcomes = 7\n    arms = len(outcomes)\n\n    with model:\n\n        # Impute followup times uniformly over the observed range\n        if np.any(followup_masked.mask):\n            followup_time = pm.Uniform('followup_time', followup_min.min(), followup_max.max(), \n                                       shape=len(followup_min), \n                                       observed=followup_masked)\n        else:\n            followup_time = followup_masked.data.astype(float)\n        \n        # Impute age using a T-distribution\n        if np.any(age_masked.mask):\n            nu = pm.Exponential('nu', 0.01, testval=10)\n            age_centered = pm.StudentT('age_centered', nu, shape=len(age_masked), observed=age_masked)\n        else:\n            age_centered = age_masked.data.astype(float)\n\n        # Mean probabilities (on log scale)\n        μ = pm.Normal('μ', np.ones(n_outcomes), sd=1000, shape=n_outcomes)\n        # Followup time covariates \n        θ_fup = pm.Normal('θ_fup', 0, sd=1000, shape=n_outcomes-1)\n        # Append a zero for no treatmnet\n        β_fup = append(θ_fup, 0)\n        # Age covariate\n        θ_age = pm.Normal('θ_age', 0, sd=1000, shape=n_outcomes-1)\n        # Append a zero for no treatment\n        β_age = append(θ_age, 0)\n\n        # Study random effect\n        η = pm.Normal('η', 0, 1, shape=n_studies)\n        σ = pm.HalfCauchy('σ', 5)\n        ϵ = η*σ\n\n        # Poisson rate\n        λ = [T.exp(μ + β_fup*followup_time[i] + β_age*age_centered[i] + ϵ[study_index[i]]) \n                         for i in range(arms)]\n\n        # Multinomial data likelihood\n        likelihood = [pm.Poisson('likelihood_%i' % i, λ[i]*followup_n[i], observed=outcomes[i]) for i in range(arms)]\n        \n        \n        '''\n        Scenario predictions\n        '''\n        \n        # 40-years old, 6-month followup\n        p_6_40 = pm.Deterministic('p_6_40', T.exp(μ + β_fup*6)/T.exp(μ + β_fup*6).sum())\n        # 40-years old, 12-month followup\n        p_12_40 = pm.Deterministic('p_12_40', T.exp(μ + β_fup*12)/T.exp(μ + β_fup*12).sum())\n        # 40-years old, 24-month followup\n        p_24_40 = pm.Deterministic('p_24_40', T.exp(μ + β_fup*24)/T.exp(μ + β_fup*24).sum())\n        # 50-years old, 6-month followup\n        p_6_50 = pm.Deterministic('p_6_50', \n                          T.exp(μ + β_fup*6 + β_age*10)/T.exp(μ + β_fup*6 + β_age*10).sum())\n        # 50-years old, 12-month followup\n        p_12_50 = pm.Deterministic('p_12_50', \n                          T.exp(μ + β_fup*12 + β_age*10)/T.exp(μ + β_fup*12 + β_age*10).sum())\n        # 50-years old, 6-month followup\n        p_6_30 = pm.Deterministic('p_6_30', \n                          T.exp(μ + β_fup*6 + β_age*(-10))/T.exp(μ + β_fup*6 + β_age*(-10)).sum())\n        # 50-years old, 24-month followup\n        p_24_50 = pm.Deterministic('p_24_50', \n                          T.exp(μ + β_fup*24 + β_age*10)/T.exp(μ + β_fup*24 + β_age*10).sum())\n        # 50-years old, 24-month followup\n        p_24_30 = pm.Deterministic('p_24_30', \n                          T.exp(μ + β_fup*24 + β_age*(-10))/T.exp(μ + β_fup*24 + β_age*(-10)).sum())\n        # 30-years old, 12-month followup\n        p_12_30 = pm.Deterministic('p_12_30', \n                          T.exp(μ + β_fup*12 + β_age*-10)/T.exp(μ + β_fup*12 + β_age*-10).sum())\n        \n      \n    return model",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model Execution"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "\n\nuse_NUTS = False\n\n\nif use_NUTS:\n    n_iterations, n_burn = 2000, 0\n    \nelse:\n    n_iterations, n_burn = 100000, 90000\n   ",
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### UAE Model"
    },
    {
      "metadata": {
        "scrolled": false,
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "uae_model = specify_model(pm.Model(), 'uae')",
      "execution_count": 50,
      "outputs": [
        {
          "name": "stdout",
          "text": "Applied log-transform to σ and added transformed σ_log_ to model.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "with uae_model:\n#     start = {'θ_fup':np.zeros(n_outcomes-1), 'θ_age':np.zeros(n_outcomes-1)}\n        \n    trace_uae = pm.sample(n_iterations, step=pm.Metropolis(), random_seed=20140925)\n\n### Model output",
      "execution_count": 52,
      "outputs": [
        {
          "name": "stdout",
          "text": " [-----------------100%-----------------] 100000 of 100000 complete in 233.6 sec",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "trace_uae = trace_uae[n_burn:]",
      "execution_count": 53,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "plot_labels = dataset.columns[5:12]",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Imputed follow-up times"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['followup_time_missing'])",
      "execution_count": 55,
      "outputs": [
        {
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc40dd73390>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc40dd73358>",
            "image/png": 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LFy5UtWrVNHXqVD3yyCOaM2eOUlNTFRUVpZUrV1738a+99pomTZqkVq1a6f3331d8fLyi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\nAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Baseline estimates for each outcome, on the log scale."
    },
    {
      "metadata": {
        "scrolled": false,
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['μ'], ylabels=plot_labels)",
      "execution_count": 56,
      "outputs": [
        {
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc40d90f2e8>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc40d90b438>",
            "image/png": 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o+PhINlvRPe7moCFdre9mffDxKekqAeDeEh8fr/j4+GJp664IG9dGMbp37643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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.traceplot(trace_uae, varnames=['μ'])",
      "execution_count": 57,
      "outputs": [
        {
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc408efbc18>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc408eaf668>]], dtype=object)"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc40d912550>",
            "image/png": 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jztJDsRhuvfXWhpYvCPXUC6IE4Y3Gcx2SQ3+F6gLGlYUiQvEV6L7obJseUnM6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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Follow-up time effect size estimates. Positive values indicate higher probability of event with increased follow-up time."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['θ_fup'], ylabels=plot_labels[:-1])",
      "execution_count": 58,
      "outputs": [
        {
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408eca470>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408f80320>",
            "image/png": 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Zufzee+91fr1582bNnTtXcXFxGjhwoM6cOaOMjAylpKRowIABcjgckqRatWqVBigZYyRJ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\ngg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.traceplot(trace_uae, varnames=['θ_fup'])",
      "execution_count": 59,
      "outputs": [
        {
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc408e18b00>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc408de1a20>]], dtype=object)"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc40d8ef4e0>",
            "image/png": 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MrzwNUBRS3hSoXjRFZ61/tvxCHjptSCfcGla9S+xaxPgIysy8a5xYvwn8AZSd\njyOAg16v+7yiomhN+q0KXlXDMgbd366miXJon/suHYY441woTLv2R93tBlAVVHsa1S4tSQ8gFB+K\nmB9kcNQIamkY6jWsnvPBH0HtjKPaFXfgxOsjny/wV//vLSsb/vyP//iPufPOO4lG3bCapVKJP//z\nP+fee+9ddoMkEonkhbLQtW9OPBUqTSmkTiF++MMf8vnPf55KpUImk2F4eJiNGzfy/e9/f1nyf+ih\nh/jMZz6DEIL3vOc93HjjjUvSfPrTn+ahhx7C7/fzuc99jk2bNr3o8hRsMBtYzz1J02ngU33oqgcF\nBWXXfHQw8gs6Mb0DKBOzI9zPI6LCQQ/V0fkoYsKxoaePmAdKuRm3cy0EYmCQ9NpVTBfqKAf3oNrz\nARUC/iAtu45jBVwRBYi2gaLYoLqde9VTxm4liEeCFJ99Em3QA+USNN31H6pRQ9GaYNSwmyncrjGI\ngTUQS6A8/QQIQTJlUqoC8QTklO6MhHLw8LUxNorudqL8Xo14xIdKgEbdhx7zo6AxWZpEWSDa2k4L\nXdXRPCWEY+C0Y4CDtv9/F+dcDaNUR4AjhzQHIFdEAEp+8f5lIp2Fdou+dAghBLVWhUpJQ8lP0ZcO\noRX2EVw/RDSVoPzbAzQy7jowpZNhojCDotdIx/1drSuiWcZbOkLxE0kN0Srvw6xpiJaOUi5Cow5C\nXyQ6u5gNelNBYp0JKlYb70CUVstmslbFrLnCRDUaKMBapYiaG8Eeeh3s3g8Igk4bVXgQuolPqaJp\ngkAzDU6bNft3c8gbQCkXEZrGmnicQ033fqQNB7/fxvSGyO/cRV/bJOHTyWYiTJVrdFohbBSi+REi\nTomRhooyM00EmAuz4VhBwH22FLUDWgshNIS12GVswvCStNpdETWneZXpSdZ7VUJOm57VMaqNDuPP\n7MA+4xx0RWFTXwDTbDNTFngNlfzoNORz7lM5PsJ43yCecgER9BOsz6+rWiSiwBW37RbKM+57qtSe\nRfQPugJj7zMAJKI+5qS/smcX4szzAGg5DuOtNj5VpTU24wp0NxUKJlQPAlBzbKa9AajWWJXdiL5/\nJwLX66LVdgc4AFZZZUb0KLH8IUpt1/V2Mwrjnh466jilSD8JbwrHPIjHtrDHmghgqn6AaG8ar28I\nlAVudtUSankPomcDQgkuElEAyt5nEIkUjrCZVgSO3kRX5qXEmB5hnVVErY/R2V2mNxVgeoGISsf9\n5KszKGrmRSZ2AAAgAElEQVQH5+nfdK9Dy40S8Bk0V6dw6jkwPHD4msCmieLUIRAEqwPjo4uGZ5S9\neyAYwpNOY008BQjsVhI7cGIjqx6XkGo0Gl0RBe5apnq9/jxnSCQSycnDbVjAY6jdEOiFchPkhPQp\nw5133sl9993H+9//fv7jP/6Dhx9+mJ/85CfLkrfjONx6663cc889ZDIZrrrqKrZu3crQ0FA3zYMP\nPsjw8DA//elP+e1vf8snP/lJvvOd77zoMoPTu4i1TSY9JaJ+AyEaNFtVWk2Vs1atwcFi38wwZk3H\nrwWwhI1/Yt5NyGNotDs2/mQc1ZpCAYJalEBA5VB5lFDcT63oJZsKEA91oH4I6tDnAbM3jKoqePQq\nyugu0kAn1aYtgpidJl7Ng6qC8KUZz1Vomg10XTAYH0Q4KroGbTVH3exgxMt4NUE42qbTMZlqLuiA\nak38Xo1oPIximYyOeRHRNKm1A2xYFUM/dwDHbFCe3k1ah2bbJnvR6ZR//STNlsXETJ1sMki10abV\nsvEEGgT9CiK5BqUwjaEG8ChxAlEFCPBsfj94VDJhL4amU2iUEZ0OjZ4Yq1sq/kgCf9WPEA7FVh1N\nUTAMtyvmU5JMl5oIR5BNBrAch73D7kyKoigIIehNuR0yn1fHa2goCtQabcx2mXTCD7gde3e+pQJr\nF4SfHh+hMj6CooBfzQKg+nSG+nqxhEnLcTvrkY3uLGXSEyaSXD97cg/VRpvhySq6tpb8eJ5scYSJ\nmTpnnn86jYlJyrPuS6nYfMCESNBDU0Bd1Ul4VTQFbAF1q0msNOV2QhWH1PQTBAbc7qAQIMY7oNbQ\nEZBKo0w0EHaARNjLodk4Pn1Kg7WFErG0n/xsd9YpzuBlhguzfqKq+zvccWrEIz68ahRdmbNNsDli\n44QT5EsNDF0lHvZxmoCnLLfz7O3rpzo8gkdXUS2blqKgaSoK4E2laOfzDIbDWI0Gg3Q4ZLs2rNNs\nCmaVtuOul9VaZbTQKOGahfKcRgB4o8fH0zMdAo0WJc0gbncwNY0ep4YRd21cm/Th0xQs22HPcAlN\nU7BtQSTowRP202h1GKl28DsO8aiP1tj8wEUs7KUnESATD9C2bLyGRsWcZHzfGL7NG8hEQpSHR8lE\nDHTNnRFqtS28Hp1Up82UZqCpSneeUrVLrO0PI4COpVFqmAwFIYKCpvhZT4j9xWGqs/dGRdBrz6B4\nw4QbRWyzSFvJszYcobnKxwE7hm6VadaqnNs3QZ7TmNp7kLVRDV2MYfpNalOPUy4GAYN4xEc2GaDR\ntHgqV6der5EJ16grIaCNz+PFcQQ+j06l3sZCIRCCVijFGLBljZeFMS/8YajWBeVyHccKMdQfoaPl\nAIFar2OLDk5LoCoKarYPUcgjVA2zWcXQVTzhMLUDDeq6uwawWHGf/dPXJBCOQFPKtNLr2D9aAhQY\nfxkEm3AcB9M08fvdB6xer2NZ1jHOkkgkkpNDs71gRirqNuALF3RLXvnouk4ymcS23Xt94YUX8k//\n9E/LkveTTz7J4OAg/f39AFx++eVs3759kZDavn0773rXuwA4++yzqVar3X0PXwxNajS0QwT9sy5S\nho7/9DX4G02GR13XIq9Hx5sAaOBxBKujKQJeL/7+fvRgAEU30ONRHvnVT6Bh0hiKgqqAs4nGjt2o\nYZNQINwt03Zsys0qcX90ibt+0zaZqk8jFriZbTj9TZw+CHse+x8AalaNertBfzRLdNNZtIojlDp1\nMJv0NlQMXSEWNfDrUZSQRrUjCHfqBPuztFpNDGMEbd06zhhIIoQbpEINBlEqKjrg9wpmpneTecPr\nKT2xg7VeHV9PD9lMmvzTj9DugD+bxYjFiJ75OyiOyswvf0WtVSewZTMMuu9+znHI1AWN53ajDSUJ\nqyrFEAQiIaYjfsRz+wj53Q676vGiqCq6rZNNBBAC9hfdTvHGNatnRRRoqTRKo4YRidDMzQfdCAU8\nhAIsQQhBvlGgaJZJB5PE/VEa7QaOgMyG0wFoDI/g6+2lNTVNqz1DcM0aAHzBDIFIfzefXdN7iPsi\nnLHOFWCnDyaA01hn2yiaRmhggFSnTf3AQTzJJKqhU9vrrrd61pof1bcFnG3YtDSwMgFsS+AgMGaD\nMWiBIEYkzKAyhT+7DnSdju7Fe7qKpxygPTNDyIH9tsZm3cZQdAIIfI6DTxHUhUJKEd1Os5FIYDkm\nzIA3mcbwhmlOzEdXUxXo7U0Q3rQRu17HHBtn3Zr1dFBIBbxMRgK0JibprZfQFUEHm73pAbzRKN60\n+97ptPAFBW8sW9QPHnLFNFBcn4Gds5EXd+2jCnTiq1EVhUY1T7mjEY+EUCJeTtdtdtsanQXeZ2Pr\nNvKauJ/Sjh1sWpsgeuaZlHftomU77LY00D2Eg+4MXxXoC/uIOjYeY36OZKReYljovFEPEbFN2quT\njJVLeCiR9sFCd1efR2cw7CNq2sxYbgXOvaFWtcqzaKzVHAJD66jVDlIPa2T8GxgpjWENj6GpPhIR\nFeG4J7qvd4MGIYSiMq2EsawwtXaDjjN/D57YN8UWo0JQr6I2DNqijqYrREMeoqEOupLBoyg4oo3q\nsfD2JcBuM9yIoEW9KP4AqxoF+jxengmlUafzpNQwHTRaTgFN8bHLn+JMr0oolSJXfBbNyBIenyQc\nhIAap+HMr9t06KAooM3+PvmmBaqSAQcCgTCmM4WYniQTHcARcUxnkoBPR8VwnzvNPc9raKwbiKFr\nKrHwglj4J4DjElJXXHEF73vf+/ijP/ojAL71rW/xjne844QaJpFIJMfLnGuf16MRD7sjfIWKjNx3\nKuHxeBBCMDg4yDe+8Q36+/tpNJ5/Xczxksvl6O2djxLX09PDzp07F6WZmpoim80uSpPL5V60kOoS\n0GHjBhSrgdADEA5Cz+zeQBN5dzR18xDKrn2MNCa48M1/jKJpFBoldk3vgTrQn1mcp6rAeZug1mD/\n8AS9epSSWca03HdiOijoaxoEjACaqlJt1ZhsTKBk4tBqQ92EbJI9M7OufmsHsMOrKM12bg4C1Ato\nlrsWg4CPTrXhzvQ0oBi1Wd/XS64ySjS8iv3VnLtEIh6hbXeYyD2Fx27jj6/DUHVadpuDxfnZtkh6\nM8HXnI1P9yKEwOw08azL4rFTqLq7zknTfQgheHbQB/igtWCDVFVlKgy85gxsu41iN1GbJcYqs9H5\n+gRVNFAU1iUHSPaciab5KD7+GxyPgeVLQqvNvrSKf98Eq1efTuKMjd3sPakUwraoPbcH4TgIAd5U\ngtD69ZSe2c2B4WdoztY1WzYwBdh788yY7mL+eM8bCHmCVEIaoeQA+poB/OZqWvWp2Wvzc7A4gq7p\nzDSKVFt1iqY7O9boNPFoBo5w8Bk+9s4c5JzsZkKhOJ54nEbHRFU04tEonXKFerWC3/AS9UWoPrMb\nAEMJ4xvKIoTArtVo5nIouoF/lTuFH4nNb947t6osesZmhBCkFYX+/AyVXbu6adadcyb1ffsJznoq\n+fv78ff30bFrOJU2RjxGNLsRzfATHFzNzKOP8WxuDyEjwKY3vJ3R+jTRSBgnsoZ1gfn9tJL9aUQ2\nQadaxYhGeWLiKexmnmfzM5yWXItKk0prHLMDPT1nMrzjF/PPgMeA3pT7Ds2yvzi/9qmlhrEMH4aq\nU09m6OTcGUGPAnY0iqMpaOEQ6YvcIGvlZoVnV3loVpt0OiGMWIyQ7qfw3G50VPY2qpQ6LYK6RkxR\nOM0f5OlsGkZz7LRUVmuCMXvxOsFIOMhZG9cxZbbpCfnxBgMIx6Hv4CgV3U+7UKAzM0Nh9t1t+WPM\nVEZJ+VxxfMDMgVeHDYOIx+vYjhdW97GaIE51lJJZJWMVGFPckOwCQSfih47lvuPAlFljyqkQ0jU0\n2jhCkKtN49W8WL40JQSJ0zbh74wwVncoHJqgbdkQDaH4bTfXtJd6zxasQgErHWdCzRD2aPis2dlc\nHGo9WQ61puiIArRhaHANxU6VRjSJf9dY1z0T3J+UoLbUnURVPIuOq7MuhUY0SmrjG6g+8SzCdid5\nYuecjR4Mkn/4F0vyWW6OS0j92Z/9GZlMhp///OeAu0nh3MicRCKRrDTNBVH7oiFXSFVqUkidStx0\n003UajVuvvlm/u7v/o5qtconP/nJlTbrxZNNQr+7F9PGYISGaTFSHsGOrJ5P05ty/wG8ZjMA+0oj\nDISSTFRzR8jUZUvP6ezMPQuhAGxcy0SjCVrYDRZRKEMmwbiqQt0k5Q1T94ZRiq4otTNr0Bpuh94x\ngih2CyeYXRJYAW3x+sN8owAiBOgwtIp97QaqgNF2GxHqR6vOjzofnJrdMHbmQNdtzvFEUNtuxLwn\nxnfCYXtUxVozxHwR/EInltnMSHmcQ6Xnj0Do2ulhTWqI4UO/oBvUYXbthWMEqfjT9BjulFLkvHP4\nzcRTYLt1bgO1oSz1bJIE7uyQQFD2CWLeGKnfuZBaq86OkSfBaBAvH8KzKkIzNAi1BkTDzE3PzJyW\ngX0t6E3x1NRzXfP21V1xF/IEiNsW07VphmJrGa3Mh2SfY3/xyOu3dkzu4nX9Z1PrNNg1Nb//VbUN\n5ZZCuVUl6ovwmtefg7J/H8G1a8iZY4zknsYOZDjz9W+jPrO7K5zXJwbRVA3TajJdLxD1hgm3ahhe\nN6CCN5XE19tLc2KCyObNeGIx9LO3kH/4EQJrByEiKBef6dqh6gaq7v4uO5rKswMGeLLUgEcn3AGL\nMdzrHYw1WRXt656raBpG1J1BbXSaGBqsjQg6nf0oCswGtOTXuadAF7DQWSqbhok8UW+YcqsK5250\nRUSjSWiqju04mFGdR0SddF+akD/MGwZ7+NZTj2EXDrExGWaqNka908DsuB4Ots/HtNOG6qy7WNoP\n7Q54/RBIUNc16sztkgUMZJkZz1NSdKK+cHeW6fxslOgZZ7hmBjv8enQH5GFVtJeClqPcVFg/kKWd\nVRjbbUKpxYzaAkIcMRjtWaexJb2BcCSBqqg4joW65yHMag5aEWi0KIV1QAHDIKPUmawEUNUmz7QN\nwkqQc9I6OydyePv7ESPTjDt1vKkEe4sHiOIw0yghesJg6xDwodttVmsTOL44M2YZQ9eYUXVCCtRt\npfuTYQuHh/fuIBoJMTfstK8+7j4PU7vZF41wbqqHqcY4rXaLHk+SqWCc+L5pDG1epnjTaVrTiwPN\neNUkgb7VeAMJPBe8nvz/PgyAHnZn4s2hXvYrBU4kx70h75VXXsmVV155Im2RSCSSF8XcGimvoXX3\njag2jr4YX/LK49xzz8Xn8xEOh5d9v8Genh7Gx8e733O5HJnM4lmeTCbD5OR853ZycpKenh6ej9tv\nv5077rjjyH/0+Fgd7cOje8AyCRh+dFVDVEcR3igIh82rzufp3LOowsZR3eY6N7OP6fHHEaoOoT4Q\nAr/hI+gJkm8UWB3tI+qLsDa+igPFETfC2ELfs+yCGbSgnzwdlGZ93slI82IHsyidBsIbRRwtYq+i\nYAd6QFFB1WFTCibLbv6KgjCC2Lpv8UJ2QFmw5wu44gRAeIKIThVFCNRWCSewIJCCY1Ft1am26kR9\nYaKqcUQRZWg6rx84l/899Gj32OsGzsGjGahrfoeDB//nsGtQydXyzDSKWI7NEQn4GK6OMxDr4+Hh\nx7qHPZrBxtQQT+Z2g+Hem7lZIwwd4u7MyupoH8PlcdA1OH3NkcsAau0GNQB/mqenXvhmwM/O7Mee\nnR0bqYIlFt+3czIRVEWQW5NiX/WgKwxmRftT03vBsiGQRmtMM17NsSrax6QSoN0Zx+w0aQ0/wsYN\nb+nmF96wnvAGdw2XEILC1E7008K0KcD8MjkcIQhGBnh4+PHFBmcSHImR8gSGZrB35iAezSDmi1Bq\nVji394xumqMGkT5jPYxMsrZvPb2rTyffKJJ453noms6B4og7I+n1gNdD3O+jUICaBxAOk50yKb3E\nL4ZHumLnB3ueYnVYsHBLrfaCx0RTBLZQ3Nkvz1E2YdY1Wr1JMJto0TBxE06PBaj3hrDNEgl/jF0L\nhPVI2XW7i3oF03X385o1EQ6WI90L92rus96xF6hGQycanX+3VVUn1XsaoUgcq1DloD+C0LzEtBYJ\nZwolliLRl6A0k8dqtigGNP6rCcR7EEqIdWckac40adKCRou5cBuK3QTdh7DbXJzWsJxeDllO1y6A\nXD1PyBMg4g1xVhz+a9x958uVGokANGwN/HGMVoFR010ytKsiCER6we8wrqggTPSoh3BHJbcqTMoX\nxRuIEV81gGIYjJrTjJQnWBffQDCUxuw08Ru+7gwiwHR9hmGr2L03W7duXXJ7PvShD7Ft27Yj37vj\n5LiE1MzMDN/4xjcYGRlZtDbqi1/84osuuFqt8rGPfYw9e/agqiqf+cxnWLNmDR/5yEcYGxtjYGCA\n2267jXA4fOzMJBLJqxqz5f4u+b06Xo/bMas22itpkmSZueSSS9i6dStXXnklr33ta5c17y1btjA8\nPMzY2BjpdJoHHniAL3zhC4vSbN26lXvvvZfLLruMHTt2EIlEjunWt23btiWN9Fz483M3vAW/vnhD\n0nXx1TgIFEBVVCKGj7OiGVrmDKNmlboeROm4IY8VxwLhMKSDThvNslnXfxaG5gY86I9k6Y9kF4kK\nVVF53cA5/HLkN4vKxXbflXPWXoQ2u2br16M7FiUJevxsTK1nX+EQpWaF1dE++iJZdFXjYHGU0coE\nrD5ssdACEZUJJoh4wxSb5W6nzPGEQPUgVI2gL8KqaA97xnaAY4HVRG0WEJ4IiPl+R1EoS+y/YNV5\nFMwSSb/rwrQ+uYa9MwcBMGYFaG84Q3jod2mWDnZnXoTH7V8cSUQl/DEKphuCWQgWiSiAtt1xRdQx\nWBXtI98o0Jid0TindzM7JnYd46x5dFXjvN4z+fXYb583naYYPDVToOMsVRm6Inh07IljFOTOGNmB\nDDXHIpY9G3P4cRRfHLVZpNis0LTcCIh7Zw66M5CAVzNIdsrkZjfXXR3rwz+70W/LanOwNIrdbC6Z\nYVxINpQmGYjz9NRzOMLp3ru23WGq7j4tv1rwPEZ9YcrN6pGyglVZ+gfdaJqZYLJ7eG18FWvjq6i3\nG0zWpplgingvlCvz9eWZFUxBA8qzzcd0A3pDbsT/sdq8QO0JCIKGu4Htoap7rC+cYbx6hMAGhg5G\niKpoUfVBKChoFo5/lkRVXJvqFgyEM1yy1hXAo+UJPLqHVCC+MIJ+l0BkAMMb4YKUDmNP07LBq/k4\nf+DtPDr2W6ICip4AiqoiPEEUq43jCYKqs8/0obLUq0NRXDH1ulQEr6bg1eD8nvWouo9fje4g5hGU\nnDi1Vpm2EWJK89Oy5uskZyVptJs4WhDVnFfck7bNOjR3YGaW6aSHaYCOSb1jwuzG2auivV3Bub84\nsmimdnNmAwl/jEbH7K6Xm2NFw59v27aNoaEhLrjgArTDwxG+SP7+7/+eiy++mC996UtYloVpmtx5\n551ccMEF3HDDDdx9993cdddd3HzzzctSnkQiOXUxWxaaqswvmlYV6k05I3Uq8eMf/5gf/OAHfOYz\nn6Fer3PllVfyrne9a9G6pReLpmnccsstXH/99QghuOqqqxgaGuLb3/42iqJw9dVXc/HFF/Pggw/y\nlre8Bb/fz2c/+9mXVObhwR50TxCrXUdj/ni1sAfH7qApKmnDg2IEaKi6KzQAfyOHNz6/z1SzPIwn\nuWFRvq/t28J4NUcqkCDic4VDKhAn3ygS8gSptesoVpOh+GoMTwB9tsN7WnItewoHGIwO0B/Jdu09\ns+f0JdcyGOunabXINwokAzES/hi5Wp6+cA/PzexnVbSPmN2g06qSCiSI+2M0hEJ/9iwKzTIRTxCf\n4aNjd2BsBwiHAQ30cBpVUbuzT443hvDOr6GJ+6PEfBE0VSO9oNOcDbmzWTFfZFE9hwIJQoEEiew5\nTNXz7C3MR1o7nNWxPhL+6POmWcg52c3smJwXSBtTQyQDcRRF4ezs5m49hDxBMsFkVyA8X34h79Kw\nzX3hHmrtOpVWbdHxkYorolRFYcPsRrvPzgrG3ueJ/tx1A51D9yGAfMNdzyU8YUSnjmK3eWz4UXf/\nrAW0zSK55rwoGC6NE/dHSQXiHCyNujOnRxFRa+ID9IezS96F56M/kmVNbKArbLOhNALRFXKv6dvy\nvOcHPQH6wj1MVKdQFcgGBJON2cAGsz3ipB9AUG4rmLa7m1WpHWEomeZQcZSm3cYz2xXWVPCogrA3\nRsiYr2i/7iXqDVNrT1PrHBbYxTqy23l/JMtUPY9X81Jr10kGYkR9EbLBNPW2xXPFOoPR+cGKgej8\nuk6OUoXG7Ptybu9m9hYOsjG9Hq/u4XcGz0cIwUBkmr35vUw0PQjdT8DwkfIn0FWNXLuKabXIeFtM\ntdz7vi5Q5/TUukVleDwhFEWZHaTZQamt4fgTKKpNoVljbcrPgbyJrmqEjCDxcB8d1YOmqbTtNjON\nEigqjQ4Ejq63u8yJqCOxa2oP2VCaydq8C2DAOEI0mGXkuIRUpVLh1ltvXbZCa7Uajz32GJ/73Odc\nI3SdcDjM9u3b+eY3vwm4roTXXXedFFISieSYmC2LgE/vNsgBn0FDCqlTilgsxrXXXsu1117Lc889\nx9e+9jW2bt3K008/feyTj4OLLrqIiy66aNGxa665ZtH3T3ziE8tS1hzhxAaqhT2EExswvCEKE4tn\nDRx7/hkOGH7WeQzasT4OFA8R80aJHNbRtto1rHadenkE2zKJpjfhM3ysSwwuSrcxvZ6W1cZQdR6f\n2IlXN9A1Hc2YD52dCaVIBd31FsdCURQ2poeA+SiHPbNiJhV0XbiEcChOurMqmqLSl9yArumLZg0M\nzUDTfdhWk8jsehxwRZ3lWORsh4WOgZvS649q35yYsm0H4Qh0Y34QWFVVsuEMlmNzsOQGuegNZ4j7\nojyT34MQEPIECXmC+A3fIqHxxtWv4ReHuam9tm8LPsPXXTA/EOntXjeApmpsSs8L3NNS61ifWIOi\nKOwrHCLuj+LTvTwzvbfbyT5cRL1x9Wv4/9l78yg5zvL+91NLV+97zz4jaTSjZbTL8oIxYBZjkkC8\n4DjE8c9wTSIH7nEcA44DCpCck9+FhJB7HMgviU0CxCeXJMRLAgESsAyCxHjBi2xt1r7MPtPT+17L\n/aO6e7qne2ZampG1uD7n6Gimu+qt6u6arvf7Ps/zfbLFXMPjuq7zzJkXK623CDr81RSwtaF+VEMD\nvVEMrgr00u3rQBTE6gq/Itkolq+5wzWr+S5BIAdImQk0dyeIMkIpg5iPoSu+hrGjmk4yZQob3T2b\n/rqlYz0lXa2K33OhIo4rKX9+hxef3Usin2QwtAqnbfHegU6bgy2dQ7w6fhCXDTZGfDhlkZlcrHw9\nGdy0bgc/PH6KdDHD6lAftpS5eLEyaEY0+rwqQYcP3dBZF8kRcASRRYHtnV4KWhGP4qao6chiH7Fc\nguMzZxhNFwks0P+2EjVrhtehsKNLafpcK3jsbrbVpEeC+XfbH2rH71DYc3qUkDPAW3o6eGXSTFFt\nC/RSLKRw6yfIahKC4qTbN/t5OjwdON0d1fuuIpnOeSu8BlM5CJU/CkN20GmPISpmJM3lCoMgoEoS\nQmYK3REAQWQ8C2uD4vxpti1SK6IA1kX6lzTeYrQkpNasWcPExMSi+eCtMjw8TDAY5DOf+QyHDh1i\n06ZN7Nq1i2g0Wk2VaGtrY+YsQp8WFhZvXjK5Ek7H7FKW2ymTyVktGi43dF1nz549PPnkk7zwwguX\nfN2uze4h1LW9+rsnuBq1lAHDIJ9pTBEqFNJIgkC7ZwVTecjkYYUbfJF1JKfNyX4yOltvkZg6WDd+\nqZBEECRkxY1dNidlV3QMkSg32BXmiJJWRFSrzB3bViOUalnTsR4tH6/+XhFWNsnGhs4hnh0xzQlc\nNmdL53dwr7l6vXF7d0Pko8vbTk7N0+PtxKWYInJH1+a6cf0OH1d2b6agFfE7TNHwlt7tPDtsit7V\noRU4ypP3a3t3UNLV6nu7EGK5++5geFX1sSt7tpAuZrCJjcvyoiA2jVCJokjA4SVZSOGS7URcQTaE\nvZR0gyOxNG5RYV1oE7F8grAzwC9GzfevwxOpvs4VftNq3W83RcCRcmodmMYTNruNw1OmnbqUqTfB\nqBiE6HY/azo3cnjmDAgCRQBHqHruLpuzGhGdjx3dm3lx9DWztqZziNPxEXRDJ+DwcTh6grAriL88\nxqaOdWi6VhVkV/ZsXXDsufjsHq5bcSXJQgqv3dP0Wgo4fQScPs6kGu8l3d7ZGkq3MhvxsMtK9fN3\nyOa5tblDtLlDqLrGaxMH8dm9GIbBQGgl4+kpCmqhzmDjjSbkCvDLg16Kmo5dltjc5uO1KfNz7feA\nR1qBER9B9/rwe0Lo5c/c4W5HmCOIB0IrOTZzipvXbaumB/udftYrMroBIzlQRZErOgIcmJbIKm7W\n6gaJQhKnzUGv30+vz0U8lyCWTyIAKwO9Zh1eLs6hafM6DDr9rPT3VKPALpujmj4793y0xPlN8285\nInXTTTexfft27PZZOX2uNVKqqnLgwAE+//nPs3nzZr7whS/wyCOPNHzJtRLuXbCY18LC4rLHMAyS\nmSKrumZXRp12mWRmeayxLVrnfBXzAnzxi1/k+9//PmvWrOGWW27hS1/6Eg7H4qvPlxKKw4/i8ANQ\nKqbQSmWL4rzBaPly7nMbnMmAKNnQSyU6O9Yht5i6kpoxJyG+8FqS0cMIgoTNbk5M7c7wQrsuC57g\natKx47j9K+fdxu0Kky4bUngCq1DKdU8VrltxJXm1gF06u9V5TdOR5fpJnyRKrAnXr1Y7mkQ0HDZH\n3eOyJHNN7zYkQaoKIjBFjV0896gBmJGwsyFTVFkdGkQQs6RLKmtDHrx2U4htVwLIopnuVxGKb+3b\nQVErYqtJtxMEgZWB2dqR0dQEmaJ57UXcYSRPhJWlfJ3BR4+vk9HUOH2+bhAgGBnC5QygKG721aYK\nAvOTwtwAACAASURBVG/p204rOG0O3tq3o/qergj0VJ+7undbw/bnGtWqIAhCVRw3I+xUiObqJ+Ei\n0O4+t+8dWZTY3rWp7rFOd1u571Pr6Y3nA4csVYWfyyazPuxF92gYmTQgc82GX0XVNRTJRiZ+EkGU\nEcVGCdHlbaerLDK3dA5hl0xhOTP2MqIA/T47/rYAgiAwFPGhGQZ2SWQi4+ZkIstIOs9IOo8sCOzo\nmo3OCYJAxB1iTcng9ZFTuNQArrCTwfAq3DYnXruH0eR4tV7Ka3ezsW0tsiQznBhuOM/lpOU+Uh/4\nwAeW7aCdnZ10dnayebOZy3rjjTfyta99jXA4XG1wODU1RSjU3NWlloWKeS0sLC5/MrkSJVWv2p6D\nKaTyBRXDMC74DerNxPkq5gUzte/b3/52Xb+nyxl/ZH011S9KBDBTpc5kAFHC4enE0DVOpfJ0el3Y\n3W0UMlMN4+i6iijKVREFs1Erw9AolqM/Dndbw77LjeLw10XImiHX1JnMXe0Gc0LVSvoWQKEmvVct\nNQqppWBbwDzhjSJdVNk/Xe+CaK/xxlaa+GSLoohDXPj92961iaJWQjd05PJnEGrbUBVSEVcIj+Ji\nbXi2VsZZFuQBh48eX2e1Z9eWzqGzimzWCtMLzWDQQzRnZkZ1uh119UnLQSyaZeSUWYu26YqeRbZ+\nY/HbbRhKBNVuR7a5EUSpKlw9wdZS5WrTc/1tG8kmT+Py9VbvybIoIJeLu9pcdk4mZhc/VcMgni/i\nt9uq2+uGwavH0zjUMJlCieNHphlcPxsZ7PZ10u1bes3s2dKSkFru9IlIJEJXVxcnTpygv7+fZ599\nlsHBQQYHB3niiSe45557ePLJJy0xZGFhsSij02YTyK7I7ATMYTfTCAolDYfScpcHi4uYj3/84xf6\nFN5wgp3bGEnlkI08suLGMHQK2WkURxBBlKpCwzAMjJq6gkD7JuKT+wCIT7yG3RWhNMd2fC619VEX\nErFGoEjy0s5pYnT2NR89OMmmK3rIpgtomoHLraAbBjbb8omrN5q5IgrAuUxiUZkjFGVJJuDwkSqm\nCbsCDdvXit5KCwqbJNdNpi9FtncEKGo6nmW4j6SSefLZEqE2dzXltEIynsMXuDj+BisIglA1q1gq\nkqzgDQ3O+7woCKwPe3n5dJToZBqPz8G+gorXZSfsVBhJmxHSZK5EEvBJEvlsCU3VkeQLK75bujJO\nnjzJZz7zGSYmJnj66afZv38/Tz/99JLSNT772c/ywAMPoKoqfX19fPGLX0TTNO6//34ef/xxenp6\neOihh855fAsLizcHx0bMwtj+2tS+8k0vV1AtIWVxyTKRKTCSLuf9CwKCIOHwmLXKtXUM+6aSbAi2\nU8zN4An2I0q2qgsgQCE73XT8ixVPsB9dK9aJqnMhGa+vmchlixw/XP9erBoM4/FdeimiebWxIH91\nwH3eIvCS7GRF+3oUhx9NLZBNDiPZXIhlg5Baen1dFNQiPRcgOrDcKJLYNLJ3Lpw6ajo11gr8CqeP\nzzA41I7Dufg1XyyoFPIqXv+Fv26T8Rxurx1pGd4jv92GK1EiCqSTedLJPBPAaYdMsaDS0zeb5pvQ\nNIq6QfHgOFdsvnD1ZdCikPrjP/5jPv7xj/MXf/EXAAwNDfHggw8uSUitX7+exx9/vOHx5W60aGFh\ncXkzPGH2E1lZI6Qc5c6AheLS3H8sLn8SiURL/Qt37drFT37yE8LhMN/97nfP+3kdjaXr6jNW+Jwk\nCiqJgpmu5rLJrPS5OJXMklU1JJuzLm3O7gxXhVSFQMdm1GIaQRCx2X2UCmkyydP4FlgpvhAojsaI\nx9kyeibe8NixQ42pjzPR7CUnpIqazt6ysxqALAhsavNhX8bUxbkIgoDbb9as2OwLp4JKosTaORbZ\nFo1IsojHaycRM6MtsWiGrt7Fr/0TR6YpFTUG1rfhcNresPT1TKqAw2Wriqb4TJbhkzG8fgcrB8KU\niirFgobDZePowUlkm8TAurNLGXaJIivtdk4VZi3ii3nT7GNifFaATpXM78FEUuWKpb6wJdKShEyl\nUrzjHe+ofliiKGKzXfj8YAsLC4vJmJlX3RGazV+vRKHylpCyWIRHHnmEa6+9lv/6r//immuu4eGH\nH2663Qc/+EH+/u//ftmOO7eIvZZsSa17vtvjoMvjZHWg3oig0+Oo1sSMp+ujL3ZXGHmOcYEoyiiO\nQDVdx2b3EGjbgHiWxg0XO5lUgZmpWRHZs3L+yWkuc/E07lZ1g7F0HlXXF9xuZs61s6MreF5FlMW5\nk80U0TSdIwcnGp4b2tJFV5+/+nsuW8IwmnTWnUOpfF87dmiKA3vHSCUa3eqWm0yqwIkj04wNmwI+\nmy4wfNKs70ol8mTTBV7fN8GJI9Mc3DtGqaiRyxSZnmjeODmbLrDvpRGGT8Wqr7nyv10QCMkyK+x2\nehTzu8kvy6g193NHTcOp114crnvfNN3gdDK76N/RctGSkJIkiVKpVBVSExMTF1VBoIWFxZuX6UQe\nmyzic89OBh3lbon5gmWBfrkQjUZ54IEHuPPOOwE4dOgQ//RP/7TkcXfv3l2tA7711lt56qmnmm53\n5ZVX4vMtT70AwJlUDn2eSVOmVL8A0Os1aycUSWRzm58dnYGG5yqRqlp84bV4Q2Zvp4Wc8i4HdE2n\nkDcNZk4cmU3fWzkYJhhu7oRnUyRKRa2lyesbwbFYmtPJLC+OxzkwnSTV5DPVDYNTydmifPsypZ1Z\nLC+GbjB6Js7x16c4uHeMwpx2HJ095neJLEv0rzXb/mTTRcZHFq5lnHutGrrBqWNRxkcS8+yxPBTK\n99J4NIta0hpSZOf+XmF8JFln+gKmg2Zl+3g0y/6XR5kcS6KqlTYMAqsjXq66so91qyOsczrptNnw\nSRIhWcYfchFum629KxiGOcZ4ivhMlp+fmmIsnefF8TjPjc6QK53fBdWW/gJ/8zd/k3vvvZdYLMZX\nv/pVfvM3f5OPfvSj5/XELCwsLFohlswT9Dnq0huc9tkaKYvLg89+9rPs2LGDZNKcaKxevZpvfetb\nSx53ZmbmgvUvfG2y+eRnKmOmtQQdNq7oCNRd2y6bhFyzkBlxmW6V8ULz1Wyb3Ueoazt21+IuuJcS\niViO1/eNs++lEfa9NMKBvWMcOTDB/pdHq9v09QfxNknbW7uxgzUbOrA7ze+J1/eNN2xzIYjXCKdU\nUeVEIsu+qSQvjceqovv1aLq6zbZ2P9s6lp4GabH8jI0k6qKiFVavjbDpih4iHbPpw3b7bJVNdDJN\nPlfC0JuL+7l1fxWmJ9JNH18uaqNehw80RtcW4siBybrvpqkmUarJsRS5rBlp9QXMVEGAYNjFhm3d\n9K4K0qUotNlsvG1NB4Io0LPSrJk6VSgQVVUmR5McOx5lYswcv5AroZU09o83pvkuJy3VSN1yyy30\n9vby4x//mFwux5/92Z9x5ZVXntcTs7CwsFgMXTeIpwoM9tVPJuxWat9lx8TEBHfccQf/8i//AoCi\nKC1nRtx9991MTzeumN5///0Njy1nvcFifQ7zWmPqSUnTSZXMBYA2lx1bCxEHmyhQ0g2eH4vhtsls\niHirzmnzMZ7OM5ktsDbkqfaPuVSYnkgtunIP4PbOtkRYs6GDIwcmWLuxA6U8cU0nTMGqlnSKBbX6\n+FJppe2CbhiLfka5GkOJF8ZiXNMdIlmcFVvLZYJgsfw0E1GBsAtXTZuOCrJNAgEoa42jB2ebcTvd\nCqsGwlVnuujU/IKpVFSxNTFXik6lGTuTYN3mznN2qcznZq87XWsu8lYNhnF77by+bxy1pNe9prHh\nBN3l+/T0ePk11DwPEJ8xa8XmGm6IokAg5EIUBURJxCFLbG7zU9A0CnmV6YkU06USYVkmp+tgGAyf\nmF0Qi02bxztffQ5b/ta48sorLfFkYWFxUZHOldB0g8Ccm1Mlta9QtCJSlwuyXH+7SiaTLadkfeMb\n35j3uXPpX9gqC/U51JuIKKh3Y5sv9W8uq/xujsTMyUKmpBLPlwg5Z1Nd86rG3skEK3xOujzOuvSw\nvZMJruoKLjqpv1gYH03MTsTm4PHZSSdNcSQI1PWNsjvkhl49FXEFZrrRUskUVU4lsqRKKlvb/fMK\n1FojEZcssbnd33S7ubxUs7K+ozNo9ci7xAiF5+9DtWl7D0cPTZLP1qfB5TJFDr46httnp38wQjZt\nXje9q4LINolspshk2QXw9X0TbNjWjSjOXhfFgsrYGTPyfeLINGs3dJzTuZcWWZT0BRxV05b1m2d7\n/e17yew9NjOVobsvUI06Aazf1Ek8lmO8XHeVLJtuyPOIvVp7eJdNQpHEulopwzDILFAXdb76HLYk\npG677bamf7CPPfbYsp+QhYWFRavEU2a6QcA7R0hVUvusiNRlw3vf+14+//nPk8lkeOKJJ/jWt77F\nbbfdtuRx3/3ud7fcv3A5a2miU2k6eiBb0nDZZvtBTdcYCXiU1kydAo767Y7E0lzjnBWEFYe308kc\nY+k8c7OGDkyn2NTWvP5LNwzOJHP0eB11KYXni4p4nCvs4vkiHsVWJ6IcLlt14jmwvg2nS2FqIsXE\nSJKBmkad82F3yAQjbmLTGfR5UqlaIVNU2Tenp9Pr0RRbygKpdv6ULJTqjESyqsYrE3EKZSEnCwI7\nuoIkCyUORutToEo1k0RZtETUxUoq2Tz9zrZIxLOzx8/JI81rjTLJAuqcWh+P147Ha8ftUThRrjk6\n8Mooazd2cHh/Y/pdMa9SyJewO87OLC4WbYyuAWzc3o0gCJRKGvI80dHK3xeYosofnBVDsk0i0u6p\nCqkKgVBrjY+l8p9AR4+fTKrAcLqEWv7+6FPsnCkW8EgSsZZGO3daElJ/8Ad/UP25UCjwve99j/b2\nxb+kLCwsLM4nibLj1tyIlLMckcrlrYjU5cLOnTv5zne+QzKZZM+ePdx1113cfPPNyzJus/6Fk5OT\nfO5zn6u6+H3qU5/iueeeIx6P8853vpPf/d3fXZKQc2pmofhrE3HWt/nw221M54pMZs2IytqQp2Uj\nAVEQ6Pe7OZGYnfAcj2fo97saFkFLTQRDptT876Sk6bw0YUZBxjN5ruk+v3VWumHwwpg57ak91kyu\nyJFYGp9tdsoiyQID69rI58yJYWUVvq3DS7jNU7cqvxC28nfFicPTBCNuelacXc1Ruqg2bYyb13Se\nL78WuyQiIDAU8TaII6AqomA2Xc9nt3FVVxDdgEShyNHY7Ge70tfaRNPijSeTKlT7RQHINhHDMLAp\n8qJpdW5PjXvmnLQ3gHzN/axWkLjn3P+aiagKRw5MNkStFmPklPkd4PU76mqlKt8tC72u7l5/VUgB\nVav3hWj13ARBYGu7nyMzadM4xqsxMZJAEURcksg6p5OJUqNhy3LTkpC6+uqr635/29vexh133LHk\ng+u6zm233UZHRwd/+7d/23I/DwsLCwuAZDnNodaxD2YjUnkrte+y4qabbuKmm25a1jEDgUDT/oXt\n7e11VuiVPorLRchmY+SUOdGeHE+xNexjTDFAEIg4Ffz2s1s1bnfbccoiB8oT9alsgVxJw9di3U9J\n07FJYrW+p6jpvDxxfou0a0kVVQ7UCJLK+RQ1vZq2eHwkjk818EsSazd2oRngdDVat5/NJNFZkxoU\nm86gaTp9q1pPmzuTXHxiWBFKJxOzbnuSINDusjOWqY9e1Frci4KAKFCXIigJAp2eS6vv1ZsBQzc4\ncypWTU+rUJvmthhVYaJI9K+JcOZkjK5eP+MjCbLpYjVa1dblbbg+123q4PV9C5hA1AizA6+M4nQr\nhCJuggukG1ZeV4WObh+yTSI2ncEXaO0aFOb5WxxYP9tfyh9ykijXR61aE2lp3AoOWWJTm4/nx2LY\nFInuvgDFbIlN67vQdYOeVJ6kfn4dDc+psjKdTjct3D1bHn30UQYGBkinzS/JSj+PnTt38sgjj/Dw\nww/zwAMPLPk4FhYWlweGYXDsbx4mfeQoQ7s+Taqcb+2dI6Qs177Lhy996UsLPv/ggw++QWeyvMiC\ngF+WSagqxYLKRDRDMWRHccis8LnOqWbJWyO+CnkVTdVIFUp1k5nYdMZsrOm0cePWPg7PpEmXVF6a\niLMp4uNQNIUkCgQdjQIlli82fXw5ODAnqvPSRJzBoLtOqOQzRfKAYJeqIm8w6CbsbCzgb5VaIQVm\nncYZYEV/ffRNU3Vy2WJD895a84cdnYFqL6hKZLGWWH42pW9zux+7JFLSdaZzRRySyJZ2f1MB55Bm\nhdQVnZZL38XI6ZMzpOZx1DsbhrZ2IQoCgihUm9nOXRiQmkSqm5lM+AIOkvE8qwZNs4rahtS5TJGR\nTJGRUzG6VwQYH0mwcnUYURJwOGwIooChG9WaJkEUcDht9KwInHXUNhB2EY9m6x5z1KQX9qwIEo64\nmxpxtIIgCIQcCjP5IqIsVqN1oigQ9Du5qi98TuO2ylnXSOm6zvDwMHffffeSDjw+Ps6ePXv42Mc+\nVi0E3r17N//4j/8ImP087rrrLktIWVhYVEkdPMTEf/0IgDP/8q+kN9wAgNdlCanLFZfr8kxjkhWR\nkM2GAMRVldOFAoGihOKQScfzaJpOpN2z6DhzWRP0sPfMDDM17l69/SFW+lx0ehw8N5Imi4BPBUPV\nWeFzVqNYlTofVTMYr4mUFHIqsixweCZ9XtL71Hnqk2rT2WoRA/a6beJ5FZdNpMvjbLr9QsiyxJoN\n7YyeSZBJmeInGctBf/12B18dA6C920d7p5kpU6xJyVsX8iKLIrIIK3wuQk4FSRBw2yRenUyS12br\nWza3+atpmwNBD22uEh5FnjcKVjuPvlRMQd4sVCK4tSLKZpcIht0EQ2d/PTYTSU6XUjVRAfD5m0eD\nbHaJUsG8ziKdHjq7/eiajlgec2hLV/U6rmX0tLkoUdt/zelW6ppVhyLn/j3cuzJIe6e3mnLY3uWt\nW9wRReGcRVSFNSEPeVVjOJVraFx+vjnrGilJkujr61tyjdQXvvAFHnzwQVKp2XzhaDR6wfp5WFhY\nXPxM/fRndT/nI1sAcM8pnnWX7VOzVo3UJc+99957oU/hvNC7IoTH5SU1HKeSQBePZukOuaspf+E2\n97yTa13T0TS9YSXaLYi0pTVq755rFDshjwPDMHBLEmud5RrCbBG3x04ilsPtVpCV+loHXdMZdDh4\n+XSClKbR2x8iV9JwnqOF8nycjDcXTLVoNYsi0pzjT+cKkIMOt+OchIbdYaN/TQRdNzjwitmHKpsp\n4ipHumstpydHk/j8DuwOuS710V+TQimJQl1q5tYOP8+Nzn4irjnn71skjVMQBDa1+ZDeIBFl6AaF\ngordMb+4Gz4Vo1TS6B8052yappNK5PEFnGeVWnkpM3I6Tmw6Q++qYN3jqwbCZ23osBDtnV6mxs25\ncnu3b16b/oF1bRx6dRxREujsNo1OxBphJsliQ53TfNSKKGDJrQEUu8zA+jZKJa1pb7flwCFLDAbP\nfvFpqZxTjdRS+clPfkIkEmFoaIjnnntu3u1ayVFerE+HhYXF5YGhaUR//hyyz0ffh27nxNf+nhX/\n+lVWtL8Lj6u5kMrkzn+hqcUs56tPB5gp5X/913/Ns88+C8C1117Lxz/+cTyeN/7GuRwodon2Lh++\nsIvSmWh1oqSeSVXvfblsqTqZr3D88BQOp63ap2b9ls46m++Knfcqu52TBXMVe/R0nEDQycyc9JpC\nQSM+E6c9bzBWyBLqrK9Jjg0nmbAX6FYUXs+ZKXavTiXOOSpV625Xaw9emx63uc3Ha1PmNnZJRNMN\ntrT7qwKHBaYFFaOKtSEPAbuNsXSekFNpuU9WrQA4/vpU1S69Yh9dYSqaZlScjaK5bdKi85UrOgKk\niuo5935y25anx9VCRKfSTIwm6/oE9a+J1PXjAjj2+lR1op3NFBEEqmljobZitV/Q5Ux8Jls1URg+\nOesLF4y4l1VEgZlWN7S1C03VFxQ0siwxtKVr3rokgBWrQ4yeiZNOFVg1EKl+XyyGP3D20bW5OF0K\nSx/l4qOlv8y3vOUtTb8kKiHNn//852d10Jdeeomnn36aPXv2UCgUyGQy/P7v/z6RSOSs+3ks1KfD\nwsLi8iH28iuU4nE6f+lGun7llzA0jRNf/wfeGX0Rl+PDddsqsogsiZaQeoM5X306AHbt2oXH4+Gz\nn/0sAE888QS7du3iK1/5ypLGbcXkaHx8nAcffJBoNIooitx+++18+MMfnmfEs8OhyHT4nUyNp+hR\nlLp7be1kvpBXic9kyaaL1V4yAIdeHa/aENcWhttFkS6bglKeVB3YO5vSI0oCumZU+8/IgkCnIYEo\nUtQ0EAQmRpOEa2pz2mw2DN1AEAWeG52h3WWn/yxTaGotwvdOzgoymyhSVFUGQx5cNpkrO4MIwmwa\nWzqZRxIE1jocKD6FONDrdeJT5GpaYi2HZ9JmfVUqx5lUrk74FVQNURDmbXRceW8ATh+P0ldbK1Uu\n2H/h1AzdKwPVCeuGSHPr+FpskljX2+tCE51KUypqdHT5SCZyeP3OBsEIZuRp3abO6u+GYdRFK46/\nPlW3faVf0OVOrXiqYFOks64fahVJEpum/TVsJy+8jSAI9KyYjaB5/HbSiUJdK4G5yDZx3t5OFi0K\nqTvuuIN4PM6HPvQhDMPgsccew+/3n7P16yc/+Uk++clPAvD888/z9a9/nT//8z/nS1/6Usv9PCws\nLN48GLrOmX/+NgAd77sRQRTpuflX+cV3fkL39Els2ST4Z9e6BEHA47JVzSgsLn2OHDnCD37wg+rv\nV1xxBb/8y7+85HFbMTmSJInPfOYzDA0Nkclk+OAHP8h1113HwMDAko8PsDbspehILhjVWGjleP/L\nowxt6SKTrjc48M0TiamNOFSwCQKMZpAMgyN5M/VHVmYn/iFZZiyWw1d2+ZrMFqrNg4cWEBKVBdeZ\nXBEMg0Qsh02RcXkU9k7E2doRYHomy0w0g2cyDz0+Ih31QjZVbbIrsG5NfVnBNd2hOtv0CrX1VZVe\nXbph8MpkAlkQWB/24m5SoD+0pYv9L5vRr2Q8z8TorPjbuK2b/S+PIgsCI6di9PaHGAx6Lrq6pWQ8\nh8utoOsGJ45M07MyiKccVZqZzpDPlaoRzemJStpi8247paJW/Qwrvy/G9GR6wfq+XLaIphnVc7qU\nSCfz1LaTqxUgazeeW7PbC8mqgVmXvFy2iCiK5LJFRs/E0TWDwaF27I7zHw29lGkpxrxnzx7+6I/+\niPXr1zM0NMTnPvc59uzZQ09PDz09PYsP0CL33HMPzzzzDO973/t49tlnueeee5ZtbAsLi0uX8R/8\nF+kjR4m8/To8q2erwMcD5dX6o0cb9vG5FZIZS0hdLrS3t9fVzcZiMTo6lj5x2b17N7feeitgmhw9\n9dRTDdu0tbUxNDQEgNvtZmBggMnJySUfu4IgCAvWQjVrFhueM1E9+OoYp4+b74834KBnpbkyXmsz\nXKGrzz/vuYiCwGq7gw6bDbckmRMppzmRUnIaiViO0dMxDN0gWVRJFlXyqkZJ06vNdCukUwUOvDJK\nMpnnSCxNLlsilchTmMmBYZDXdPZOxJmJZnGWm/2OjySZHE8xOZ6iVG5fUHlruudZ7RcFgTVBD+tC\nzdulpMvj5FXTHEI1DPZNJ4nlizw3OsOrkwmGkzmGk1kEQWDF6tkoVEVo+AIOBEEg0OOlZJjjiEBw\nmdO4lko2XeD08RkOvTbO4f0TlIoaJ49Ms++lESbHkoyejldFVKtUTDjArAtajLkNVudy7NAUJ49M\nc/zwFPteGuHIwYllbXZ9vohFM5w8GuXUsdk+UbX24a1a5l+sOF0KdodMIORiw9ZuNl3Rg8Npu+Rf\n1/mmJZmZTqeZmZmpptrNzMxULcuXytVXX12twZqvn4eFhcWbFzWd4dT/909Ibjf9v/3RuufGHBE2\nAZnjx2l7+3V1z/ncCqfHU6iaPm/XdYtLh2AwyM0338y73vUuwKy1vfLKK6v26Odqgz4zM3NWJkfD\nw8McOnSILVu2nNPx5qOvP8SZE+ax123qYGIsRTyapVjUGD1TP3ldt6kDmyIT6fBw4sg0xTmmKooi\nm65hYTP1bmhLF/l8CcUuI0kioigQbvOw/5XRunTACjZRICDKDA6143CaRgyHXh0nJEkcjpu1Uvlc\nCWe5fuv1aIp82cFuc5sPV7meZ/jkDIYBT+0foWdFgHSqgF+S8ckS2ay5fyyZBwwkYfZvtJJyWCyo\n9K4MVifZjgVWxitpcxUb5FrihRLtbjuqrtc9fnjGnMfkVI2RdK48jh1fwEn3ikDVzWy0WERwuMir\nGiOait1pQ8+rbAp4UIsakiwyMZrE7bHXNUq9EBw/PH9rmsmxxjTIZsg2EbU0+14lE3k8Pge6blRF\nleKQq9ed4pDp6vFTyJcYHzE/u3yuhMPZKDIrtYBANUW1kFNJxvMX/L2rxTAMMOr7IFUa09ZSaeis\nWFGbNy0tffIf+chH6m5ge/bs4Xd+53fO64lZWFhYAIz+x/fQMhlWfuQulED9ivSoaK6sZ06eatgv\nULZTTaQLhP0Xzw3a4twYHBxkcHCw+vuv//qvt7zv3Xff3bT34f3339/w2EKrr5lMhvvuu49du3bh\ndi+vxa4/6MTn70Y3DCRJJF+u7zt6sD7yNbC+rerUZ7NJrN3Qwb6XRuq2aZ9jGiHJIu4m9sIbtnZV\n3ekqrzs+k63Wf1QmwrIs0bMyUDeR9CFSqajI19iAT2QK9AfM83O4bKTieQzdqI65ymn+LUqJEtGi\nRqoszHJzRA6YLoZuj0KybC3dSp1Gf8DNzLg5QQ87FaK5IrF8kRPxTNPeTnPJllREAew+O4ZhMFYq\nkcEgjcHxcrqgLIsgikxPpquNRMGsDzpzgmpd2/mm8rmv29SB3ILhxXy0d/uq4rV7RQB/0Imm6kyM\nJknEcsxMZbA75Drjk75VQUbPxOno9ldT9Lx+R1VInToWZe3GjrpzKpW0ulTJWs6cmGFm2s6q1Ppr\nnwAAIABJREFUwfCyR0DiM1lcbuWsnOcOvTaOpup0rwgQirgpNmmlsXIgjMdnp6vPvyxmDBaXJi1d\nVXfeeSc7duzghRdeqP6+bt2683piFhYWFloux9h3v4fs9dL1y+9reH5GlcgpbpTTZxqeC5YtVmNJ\nS0hdDizFBr3Sq7AZ4XC4JZMjVVW57777uPnmm7nhhhtaOu7ZusoKooBUtqXr7vU3RBcqkai5rFgd\nYvhUDF0zaO/2LVpwXj2eIDQIrEDIZdoTz5nLBkIuRk7F6bDZyOk6SqqE7LeTU2tqZgyDgyejHMpN\n4u/xMDaeICzPRiVsNRNkuyjiz2pU4hNDHX5KiUahUyvebC0IKbkmgrDS5yKaM0VVKyIK4FiNFbvR\n7kTJy/jLAiJVMifTbq+dQEmoE1G1pJL582bxXOFwTc3c6/vMnx017qUD69uIRbO0dXjQNIOp8RSJ\nmHm+3SsCTIwm0VQdp9tW16cqFDEXCCRJJBhxV/cZO5Ogs3c2JdTpUhhY19gGZ8XqEKePz1AqR1Jr\njQ1qeyE1I5MqMHwyVm/wsUQqFuXQ6HA5l+nJNIoikcuV0MppoKOn4zhdtrpmthU8XjuCYEZ3LS5+\nzperbMvyvLe3F03T2Lhx45IOaGFh8ebj2TMv8a1X/41YLsHbV13D/7Xt11DkxR2sxn/4FGo6Td8d\nH0Jy1oshVdMpFDUy3jDO6dNouVzdNqHyRGYmufRu8xYXnnw+z3/8x39w+vRpVHV2dfhcU/oqvPvd\n727J5GjXrl0MDg7ykY98pOWxl+Iq26xBZTMRBeALONmwjCvizYSYIAhs2NbN0UOT1ZSuLe1+sz5K\nN/jxL05Xa4cAsmUHuKhqxq3abDaCklR1CQNwSxLrnE76+oP4g2atSSFf4tTxmYZ0RWBBW+daruoy\nJ++iINDmsjM1R0StCXo4EksTdCiIAkRzRSJOhelcfUqgIAnV9MUKbpvEVR0BDsQaG5tWOHU0yoZt\n3fP2UzIMA103WnJha0btZ1BLxfTA47ObVtPlRuU2zGhRIpYjEHYRirgJhlxEp9IEw+5q9FO21Z+P\nc05qXqX2ab5aNaDOKj02na0TUhUzlNq0yRUDIURB4ORRs+4oEcvRN6cZ8lKoiCgwHS7XbuxoiEwZ\nusH+ir1+E5qJKGj9erS4ODhfrrItm028//3vr94QXnvtNT72sY8t+8lYWFhcfjx54D/5f5/5GtFc\nHI/i5qljP+Orz31z0eJiwzAY/8F/IioKXb/S6M5WsTbP+81i+uxwfXqTJaQuL+69915++MMfIkkS\nLper+m+p7Ny5s6nJ0eTkZDWF/cUXX+S73/0uzz77LLfccgu33norP/3pT5d87MUY2tJV/bli+HAh\nEUWBtRtmDT7UkoZDltDSRVY75ndgU+wybQ4bvauCrBqIMLC+jUiHB2/AYaZFBWc/R7vDxtoNHQxt\n6cJXrpkRJaGh6emC5ykIVSe9VX4XG8KzqY7bOwKEnArXdIdYG/Kwyu+m1+tkhW/+a8ldEwlrc9nr\nmpzORyKWbXgsOpXm4Kuj7H95lIN7x8idg6uopunz2lRXaOayFgi5GNraVbXnFkSBSIcXSRZxeRS6\n+vysHAjX7SPJIpuu6GmIEM0VXHX7SCJtXeb77fIodTV4ejkF1O2xs35zJ72rgvj8Tjw+R1065PCp\n5g6CrVIsqBw+MNGQ8gpweP8EZ07MVJssG4bB1GTrNf8ev3mdS7IloixMWvpm/spXvsJjjz3Gzp07\nAdi8eTOnT58+rydmYWFx6fOfR37CP73270RcIXZdfy8d7gj/e89XeW74ZZ458wuuW3HVvPumjxwl\nPzZO2/XvwOZrdOOqWJsb4XY4DrmRUbxrZmtown5TSEVb6OJucfEzNjbG9773vWUfdz6To/b2dh5+\n+GEAduzYwcGDB5f92IshySKr10Y4czJG36rlS3daLg69Ns6Gbd1MjJl1L502G+OlEmFZximKaEDb\nygBr2uvt0WujJfMhySIr+kOwxOiEKAh47TZsokhJ1xsa4sqiQI/XFGzNolcAqwPuapPgipFGe5e3\nat5QEQHZdKGajplM5Bk5Faezx4fH50AQGhv7Hjs0ddb1VHONRyr7nzk5U0019M2TyjxfBGyx9DR/\n0Ekq6SJebujsdi9sWx4Ku5gaS5FNFzlxdJrVa9vIpAo1tW5mT6RAqF68Vnp4xaNZele2LpznMjWR\nqovYOd02Orr9nDxifjaJWI5ELIc/4CQey1Xrw8B0Z6ycZ230FKB3VRCvz8HEWHJBe3eLNxctx5Xb\n2uotVBXl4mksZ2FhcfGxd/wA33j52/jtXv7oXffT6+vCJtn4v6++C0mU+OdXv4Omz9+TZOZ5syYz\nfO1bmj6fLq/KCm3mCnnuTH2dVCUiFU00r2OwuLRYbsvxSwWXx866TZ1NHdAuFJ09s8LowCujlArm\n37Ffls1UvYDbtE7vbRRRF4rtHf5qyt98rA64GSpHr0RBYEdngE0R04VwW0eAgYAbbzm9sq2jcXHH\n5bET6TQn2KnyZHx8JMnRg5McObC0azeZyDF6Jl4VS3anXBexrBVJLvfyz896VwaxO2XcXvviTV9r\nUt6y6SLpVIETR2br/eYTdOs3z74evcbARFU1kvFcNYshly1y8NWxalQJzHTHY+XmwNk5bS96Vph9\ntOY66xULap1Ve2evnxWrw6zd2MHgUDvtnbPXrj/oJBByIcki3X2BszKusLi8aelKcLvdTE9PV51U\nnnvuuYbO7xYWFhYVxtNTPPTzv0cSJH7/bR+jwzO7ENPhaePd/W/lR8d+xvMjr3Bt346mY8Rf2Ysg\nSfi3NreZrvSIspd72WXPDNc9b0WkLi/uvfdebr/9doaGhrDbZ1fE//Iv//ICntWbk3C7h8nxVENj\n343bui/auhFBEOb6ZzTFZ7cxFPbilCVkUURWzEm/XRKxu2avO0EUGBxqbxAVbe1epscXThWrtbrX\nVL0lc5DTx+pt+dcM1fdQqxUP5+szmHvM+ZDE+tdzskZEidL851ZbU3Zg7xjBiJueFQFOHYuSy5Tw\nB52kUwWzxkwzGDuTINzmIZXIV9Md56bzDW3pqr6//WsixKLZagQqlytVo2AA4TbTaKNWJLV1efF4\n7U1dLy0soEUh9alPfYqdO3cyPDzMXXfdxcmTJ/mbv/mbcz7o+Pg4Dz74INFoFFEUuf322/nwhz9M\nIpHgE5/4BCMjI/T29vLQQw9Zgs3C4hIjnkvwpz/9P2SKWT5+1V2sjaxu2Ob9a9/Nj479jB8d/VlT\nIaWm06SPHce3fh2yq3maSkVIuSMhZK+X7Kl6C3SXw4bbaWMqbkWkLgc+/elP8573vIcNGzYgSYu7\nt1mcPwRBYGhzV12Bfke376IVUWeLz95a9K9ZlHCuKFoxEKqKIJdHoX9NBEEQGBs2ezVl0gV8ixiF\nJFuIqgfDLsayCTq6L3wEUBAFQm3upo1/1ww1Ov3NR2w6U2cWUXEQrKXWlW8uc9MmbTaJ9k4vNpvI\nyKk4yUS+KqKCEXdT2/WOrgv/flpc3LQkpLZu3cqjjz7KSy+9BMD27dvx+c794pIkic985jMMDQ2R\nyWT44Ac/yHXXXccTTzzBtddey86dO3nkkUd4+OGHeeCBB875OBYWFm8cqq7xo6M/5YkDPyBRSHHT\n+vfyrtVvbbptt6+TobY17Jt8nclMlHZ3fZFz4rX9oOv4t2ye93jJjJm77vPYcfevIvHqa6jpDLJn\ntr9PW8DJeDSDYRhWd/ZLnFKpxOc///kLfRoWZQRRYGB9G9lM0bJ/nsOmK3o4c3KGQNCF1+9g7cYO\nbEp9n6dwm4eJ0SSq2tg/S1N1VFVHUSRmopmG2qpK+mAt4TYPXp+j2iD2QtPdF6C7z2zCXIlICaIw\nr/NkhY3buhd00JvLfCJqISr1eZkaO/aeBZwILSwWYtF4sqZp3HrrrXi9Xq6//nquv/76JYkoMOut\nhoaGADNtcGBggImJCXbv3s2tt94KwK233spTTz21pONYWFi8MeTVAv/7J3/JN17+NnmtyF1bb+PO\nLbcuuM/1q8zap2dO/6LhufgrrwAQ2LZ13v3jaTMiFfDa8QwOAJA+erRum46Qi3xRI5E+e3csi4uL\nbdu28frrr1/o07CowelSLBE1D32rQnjL6cWKXW5YyHGWez6Nno6TrnEWzWbM+p8jBybY/8pog4gC\nCIWbN4NudpwLjcdrx+UxhUsldW4hBFFg47Zuwu3u6n6V9/FsaCY2K8ztR7bQthYWi7FoRKpiNVso\nFOry0peL4eFhDh06xNatW4lGo0QiEcAUWzMzM4vsbWFhcTHwjZe+zYGpI1zds417rroTn33xG9M1\nvdv4uxf/if859QK3DM022zUMg5lfvITs8eBdu2be/eMpc/Lh99hRNgwx8sS/kXhtX5346io3lxyb\nzhDwWjnulzKvvvoqt912G/39/XX3oscee2xJ47aSUl4sFrnzzjsplUqUSiXe85738MlPfnJJx7V4\nc1ObBnnyaJShrV1Ikkgs2mibDtCzMkAqWcDnd1xyRgd9q4KkkgX8wdZ6nQmiQFdvfYRoYizJ1FiK\nQNiFx2vH53cwNZFmatx0TvT47KwajDAxmiSVzNcZRcxlbvqllb5nsRRa+mvs7+/nzjvv5H3ve19d\n344777xzSQfPZDLcd9997Nq1C7e7MT/1YltZsbCwaORo9CQ/PvEMqwK93H/tbyFLrd3k3YqLrZ1D\nvDj6GsPJMXp9pmNT5vgJitPTtF3/DoQFamFiKTMtI+hzIG3ciCDLzPziRVbeNfu91F1eLR+Lphnq\nv/jsoy1a5w//8A/Py7iPPPLIoinliqLw6KOP4nQ60TSNO+64gxdffJEdO5obpVhYLMbcZr0H987f\n4BcgGHYTnCcSdbFjU2RCkaWJv44uX4Pg6ej2EQg5KRTUquV7R7evpTqxQHjWzt2aa1oshZaubE3T\nWLNmDcePH1+2A6uqyn333cfNN9/MDTfcAEA4HGZ6eppIJMLU1BSh0OITn69+9av81V/91bKdl4WF\nxdnx+IHvA/CR7be3LKIqXLfiKl4cfY3/PvU8v7H5ZgCizz4HQOiaqxfcN54q4HLI2G0S2JwEr9jO\nzPMvkD5+As9qs/lMdzkiNdqk6Nli+XnPe97T8Ni9995bbea+FK6+euHr4VzZvXs3//iP/wiYKeV3\n3XVX09pcp9OcqBWLRXRdx+/3n5fzsXhz4HQpdK8IMHo63vT5Sp1VYibHwPq2pttYmA2c7Y6zbw3Q\nuzJIuM2N3IJjooXFQiw46/nTP/1TPv3pT/PFL36R//mf/+G6665btgPv2rWLwcFBPvKRj1Qfe/e7\n380TTzzBPffcw5NPPtn0pjyX3/3d3224SQ8PD7e0r4WFxdIYTU3w4uhrrAn3s7F97Vnvf1XPVpw2\nBz8+8XN+beMHkEWJ2Au/QJBlAtu3LbhvLJUn6J3Nne9433uZef4Fxr77Pdb83r0AdEfMiNTIVOud\n6y3Ond27d9Pb23texk6lUnzta1/j4MGDFAqzReKPPvroksadmZlpKaVc13U++MEPcvr0aX7jN36D\nwcHBpttZWLRKKOImlcxXe07NpW9ViL5Vb+w5vZlYrCm0hUUrLCiknnvuuerPX/7yl5dNSL344ot8\n97vfZe3atdxyyy0IgsAnPvEJdu7cyf3338/jjz9OT08PDz300LIcz8LC4vzwwyN7APiVte86p/3t\nssK7Vl3L94/8mJ+dfI63+teROXGSwLat89qeA5RUnUS6yIqO2RSO4BXbcXR3M/XTn7Hq7g9j8/kI\n+x0ossh41IpIXers2rWLgYEBTp48ye/93u/x+OOPs3Hjxpb2vfvuu5menm54/P777294bL40H1EU\n+bd/+zfS6TQf/ehHef755xeNklkZExaL0dXjbxBSoRZMGSwsLM6O85UxsaCQqnSRnvvzUtmxYwcH\nDx5s+tw3v/nNZTuOhYXF+SNfyvPjkz8n6PBzTc/2cx7nA+tv4EfH/5t/3vcdBpXrAQjuuGLBfWJl\nl6uQbzYiJYginb/0Xk5+/R+Y/u9n6PqVX0IUBTrCLsbOwSLX4uLi1KlTfPWrX2X37t184AMf4MYb\nb+TDH/5wS/t+4xvfmPe5s00p93g8XH/99ezbt29RIWVlTFgsRq1xhE2RWDUYPqdUNQsLi4U5XxkT\nCyaHFotFjh07xtGjR+t+rvyzsLB48/LjEz8nV8rz3sG3n3VtVC0RV4hb1t9ILJdg/9NmvdViQqrS\nZDcSqLfFjZSj5tGfP1t9rDPsJpNXSWctC/RLGUUx03BsNhvxeBybzbYszq6VlHJg3pTymZkZUinT\nHSyfz/PMM89UW3hYWCyVdZs7Wb+5k3WbOi0RZWFxibHg7Cefz7Nz587q77U/C4LA7t27z9+ZWVhY\nXLRousb3Du/GJtm4ceAdSx7vlqH38dzxF3Ac3YfU2Yazp3vB7adipttSW9BV97g9EsY9sJrkgYOo\n2Syyy0Vn2elqPJplcJ6ceMPQUYtZdK2AIMrYFA+CeHE0trQwWbVqFfF4nF/91V/lQx/6EF6vt+XU\nvoWYL6V8cnKSz33uczz88MNMTU3x6U9/GsMw0HWdm2++mWuvvXbJx7awgMa+RhYWFpcOCwqpp59+\n+o06DwsLi0uI/zn9CyYzUW4ceAc+h3fxHRbBJtn4X2wko+1jf4/ElbqGvICQGSm78FX6RNUS3HEF\nmWPHib+8l8h119JeFlsTsSyDfWZvkkI2Sip2jHTsBJnkMIXMFIahVccQBAmntxt/2xCRnqtQHFbX\n+wvNl7/8ZcCsd9q8eTOpVIq3v/3tSx43EAg0TSlvb2/n4YcfBmDdunU8+eSTSz6WhYWFhcXlxaXV\n1c3CwuKCo+oaj+//PpIocdPQjcsypmEYiP/9CgDPdhfxHfgBt2/6wLzbHxsxLYNXNWmkGL72Goa/\n/RjTP/vvspBy4rUXyEzv5dT+/yEZPUIxH6tuL0oKTm8XiiOIJCvoukohO0M2NUI2eYax408R6bma\n7oH3YWuh0bDF+SWZTBKPx+nt7UWWrVuYhYWFhcWFw7oLWVhYnBXfP/w0Y+lJbhx8B+3u8LKMGX9l\nL6nTR/C8YzODnXmOH/0R+4wMPtmOruXRdTNaJIoSouTEXRrj2gE3DqZRSyEk2Vl1W3P0deHatop4\n6iBHnv17nPkJPvXOGKgwPQKS7CTQvglvaBBPsB+npxNBaCwX1dQ8sfG9jJ/cw/Tws8TG99K79gOE\ne66yGji+gTzwwAP89m//NuvXrycej3PzzTfj8XiIxWJ84hOf4Pbbb7/Qp2hhYWFh8SbFElIWFxzD\nMEgnC8RmsqSTebKZIvlcCbWko6o6uq6j6waaqqOWNEoljUJBpZhXKRY11JKGphmAgSSJ2B02XG4F\nX8BBMOwm0u6hrdNLMORCEK0J8FLYO36Af37tO/jsHn594/wRo4XQdZVscphM4jTZ5Ai51Bi52CiO\nj65CJcMNAIpCYewXTM0zxjtXm/8ffPZVAARRRhRtGLqKrpfgOhEbYZLJQ4iSncNTQXRbL7e87124\nfD1NhdNcJNlBpPcawt1XMjX8c0aO/CenDvwrsYm9rNz46ygOqyHrG8GBAwdYv349AP/+7//OwMAA\nX//61xkfH+d3fud3LCFlYWFhYXHBsISUxRuGpurEZrJEp9JMT6SZmkgxNZ5iejJNqagtPsBcBFAU\nGZsiIUmmQCoVS8RjOTRVb9hcsUt0dPvp6vXT2e2ns8dHpMODLF/YQl9DNygWNYpFFU3Vy60GBGRZ\nRLFLKHb5gkdANF3jyYP/yWP7v48oiPzetb/Vcm2UpubJJkdJx0+Qjh0nHTthip0yhi5QSmhIrnbc\na7Yg2Hy8PHWSFyaOEvZ28f51v4Qo2dENA01XOXB8gpcOnuaKNR46AqAVM+hqFsEoocjgUBx4XF7U\nfccpPPUsve/6Ff799RBBr507/X1n/doFUaJ9xdsItG/i1P5/JRk9zIFnvkzvupsId195wT+byx27\n3V79+cUXX+SGG24AoLOz03rvLSwsLCwuKBelkPrpT3/KF77wBQzD4LbbbuOee+650KdkMYdSSSOb\nLpLLFsnlShRyJfK5Evm8av6cV8nnSmQzRTKpAslEjnSqAHPakUmySKTNQ6jNTTDswut34HbbsTtl\nbDYJSRIRJRFJEpAkEdkmYrOZ4sKmSE0nUoZhUCyoJOJ5YtMZpifTTIwmmRhNMHxyhjMnZi2TBVEg\nFHYRavMQCrvwBZx4fHZcbjs2h4wmC5REKAFFAQqaTlHTKek6umGgGyAAkihgE0UUScAhSyiCgKTq\nCAUdPVcilyqSSuZJJwukU3kyqQKZdNGMvuVLDe9LLYIo4HIreL12fEEngZCLYMhFMOIm3OYmEHIh\nSYtHWM6VeD7JQ8/8HQemjtDrCvBbG3+ZtsI0pw48TiE7TTGfQFNzGLoGiBQlFxncpHWFlCqS0URy\nhp08dvJ0oYqDlEQnRRSKGhRUHSNQPv9TlaNuBMdGMiX4m31zLa4lWNnPfxeBSYC2xpOeAQLd8Gtv\nQynkCV4hkc1qfO3lE7S77bS5zH8dbjtepTWhqjgCDF7x20yPPMfw6//Bqf3fJjr6C/rW3YTL13OO\n765FK0xMTOD3+3n++ee57777qo8XCoUlj51IJPjEJz7ByMgIvb29PPTQQ3i9zRcJdF3ntttuo6Oj\ng7/9279d8rEtLCwsLC5tLjohpes6f/Inf8I3v/lN2tvb+bVf+zXe8573MDAwcKFP7U1BsaASn8kS\nj+VIJXIkE5XJf6E8+S+QzRTPKoIkySJen50V/SFCYTfBiJu2jnK6XdiNWJNuZxgGJd0gp2oUVFOw\nqLpB0TCqWkPUNcS8jlgQkEQBSTCFjCiY/wRAEMARdNARsBMZCDKoGxQ1nWy+xNhUmompDNOxLDPp\nPKfyJY4YBbRECT2XRp8W0RQRQ14+cSIWNcSijlTUkDQdyabjCNpwhRU8koRHFnFKMi5JQBYEMEBV\ndYpFjXy2SCZdJDqdYXw02Ti2JBAKuwm3e8z3tcNLW6eXSLsHuUVbXV1XKeUTFPNxSoWk+a+YIp6a\n5NjkId6Cyi8FvGiIjB75KUcMJ2lcpA0XWbGdNB7ShpOUbkdl4WNKOjglGacs4sgmYHoaX3cnnvYI\niiSWBamIKMC+iQOcip/GJdu5qnczff4unvjxUVKZIh/9wEYkUUTHQNMNNMP8jIuaTl7Vyaoq8ekY\n0WiCrNuHEnLy/Fis4Xycski720Gn206n20GHx0GH2067y45jTrRSEATaet+CP7yO04f+jcTUAQ4+\n+xCB9k20r3g7nmC/FSVZZu655x5uueUWbDYbO3bsYHBwEIBXXnmF7u6FbfJb4ZFHHuHaa69l586d\nPPLIIzz88MM88MADTbd99NFHGRgYIJ1OL/m4FhYWFhaXPhedkHr11VdZuXIlPT3mCu/73/9+du/e\nbQmpZcIwDDLpIvGZLLFohlg0y8xUhpnpDDPRDNn0/E1LJUnE7VWItHtwuRVcbgWnS8HhtOFwyjic\nNuwOG3aH+bPTZcPpUrA7zMssq2rEckVm8iUm8kUOxZLEx6LE8yWSxRKpgkq6pKLqC4RnlhM7YFeA\n2d5CIuAQBBRDQNENZM1AUg1EVUcomf8MVcco6Riajq6ZAk8QBZAFBFkERUKwSxh2Cd0mocoCRadE\nzqGTr3ltqXlOSxbBJUs4ZAWnLGGXTWHhFwUEA7SihlrUKORLFHMquXyJYzmVw2oeRvIwMm2GyQRQ\n7DJOp4zDaaAoIMsakqgjUELXSuha0fynqxgI6AjoiGhIlJBRjV4K9FPATl5TMJr18C5raq8i0+2w\nEXQohJwKfruNgN2Gzy7js9vwKBJum4xdEhEEgcJ0lJc+/v8ge73sePj/INoaG1EaQ708efA/+fa+\n/+CHh3+Ox+Ylng6xMbCNG1Z3tPQxn/n2Y5z+h79DlSSmfB343/l25Le+lagOk5kCE5kCI6kcpxLZ\nhn39dpk2l52I007YpRByKAQdNvwOO+0b/hfh1HEmjv2Q+OQ+4pP7UBxB/G1D/P/t3H1QU/eaB/Av\neSHhJQQwELh6y17L3Opt0W5rsaNbaHkJIvJWZdvuTHWk1brTFlGndGpHa8fWP3CGdtbuTkGrbN/s\nTlt17Nr29hYLtmuLdVTEKk7duhVqE0CEQCA5JHn2j0iuQJATCXl9PjMZSOZAnudJ8ju/J+d3TnTc\nnxAZMwuKiHhR52OxieXn52PBggXo7u52nisFAMnJydi+ffuU/39DQwPef/99AEBpaSmefPJJl42U\nXq9HU1MT1q1bh3379k35eRljjAU+v2ukDAYDkpOTnfe1Wi1aW1t9GJH/uNY1gP4+M6w3Lrrg+GmH\n1er43TbqccdFGQSLzbHEblDA4IAF/X0W2Gzjzx+SSMKgjotA8kw1YuMjoY6LQExsBFQxSqhiFIiO\nUUKhFLcE6lyXET909sHYNQyjxYo+yzB6LcMQXDzvCIVUghiFDH9URiBSLkOEXAKFVAq5JAwyiePo\nxMhTEwG2G8vqRo5EOO47bkSOlXKSMCAMYZBJHLeRox1KmQRKmRRRcikiZDLnBF+lkCFS5nq5oKcM\n2+wwCsPos1hhtDjq0y8Mo1+wol+wYkCwYmjYBtOwDWarDb1mAcKtGksFAIUcUI9vQgDABMB5DEYA\nIIx85CPEBUx2KKRhUCuV+INCBlW4DLEKOdQKR8MUFyFH/I3GKVzE8kLr4BB6L16EWa+H4a9/g10Q\ncMe/POayiQIcR4Ae/Us+FvxhHj5t/Ru+bz8FWdKvuIhfseGLZvx5xmxoozWYERGHlNhZ+Ie4WeP+\nxx//eQVUc+7CifpPoL78MyIP/hekR/+K3Bc2QH3/PQAAOxGuDQnQD5hhMFlgMDl+dg1a8L/XTbh0\n3eQ6PgCR8gxEyghyuwkSkxHhJgvk/3cJcrRBHmaDQh6OcJkcCrkcClk4VOFyzImVIlwQQUw4AAAM\n/UlEQVSuhFSmhDIqERHR4prCUJWQkICEhNFLOLVaz9Ssp6cHGo3G+Tw9PWOXkjrs2LEDVVVV6O+f\n6CsQxhhjocbvGilPsNkcX5Hr9XofR+I5fdeH8J//cfy2/14qlUAZKUd0tALRMQqo1BFQxymhjo1A\n3IwoqNTKUUvs/s4M87AZ5mt9op9r/6lfcMU4BMAx0YySSxGjkEGtCIdaIYNaIUeM0nG0QhXuOFqh\ncPscn7AxP8UiOA6h2By/DjtuVjgajvELv6ZHOAANAI0EgBKAMgyA/MZtTMQ3ljta7eQ8N6u/rwMd\nF/971KlVNy2QvLG8UQJ5eDQk8mjY7Epc+F2PQbMUVnMEbOYI2AYjQda/H40Lc1z4ECBCmP3G7cYT\n/ONDf8KDGbNvejYbgCHAPASrGejsFZf3pX/7d/S1nnPe12Q+BOGuP6Ojo+OWfycBoPxtNozf2fDQ\nPylhiezAhY5LuPzr5VHbvZ7zItSuLoIRH4eZa1diW+3/INVwHgu6L8D0/n7c9fy/jtosDkCcHJgT\nKwFiIwBEwEaEPssweoaGYbQMo88ioF+wwShYMShYYTLb0Gu1YnDYBhtJcOsm1Q7Aguyw40iSdANw\nvE5/WbQJYZLAHY5HxtqRsdffrF69Gt3d3eMer6ysHPeYqy9SGhsbodFoMHfuXDQ3N08plmDcPzHG\nmL+a7v1TGDkuEeY3zpw5g127duGdd94B4Fi/DmDCC07s2rULb731ltfiY4wxJt5zzz2H559/3tdh\nTCg/Px/vvfceNBoNurq6sHLlSnzxxRejtqmpqcHhw4chlUphsVhgMpmQm5uL6urqW/5v3j8xxpj/\n8sT+ye8aKZvNhiVLlqC+vh4JCQkoKytDTU2NW+dImc1mzJ8/H1999RWkUt9e2tobsrOz0dDQ4Osw\nph3nGVw4z+Bhs9mg0+nQ0tICpVLp63DcsnPnTqjVaqxduxZ1dXUwGo0TXmwCAE6cOIG9e/fe9lX7\nQm3/5I5Q+KzcDq6La1wX17guo033/snv1pJIpVJs2bIF5eXlICKsWLHC7QtNjBQqJSVlOkL0S7Nm\njT83JBhxnsGF8wwugdZEAcCaNWtQWVmJTz/9FDNnzsSbb74JAOjs7MSWLVtQW1vr0ecLxf2TO0Ll\ns+IurotrXBfXuC7jTdf+ye8aKQDIyMhARkaGr8NgjDEW5GJjY1FfXz/u8cTERJdNVHp6OtLT070Q\nGWOMMX/H1+VljDHGGGOMMTdxI8UYY4wxxhhjbpJu27Ztm6+DmC4LFy70dQheEyq5cp7BhfMMLqGS\npydwrVzjurjGdXGN6+Ia12W86aqJ3121jzHGGGOMMcb8HS/tY4wxxhhjjDE3cSPFGGOMMcYYY27i\nRooxxhhjjDHG3MSNFGOMMcYYY4y5iRspxhhjjDHGGHNT0DRSfX19KC8vR15eHp566in09/dPuK3d\nbkdpaSnWrVvnxQg9Q0yegiCgrKwMJSUlKCgoQE1NjQ8inToxuer1eqxcuRIFBQUoLCzEu+++64NI\np0bse3fz5s1YtGgRCgsLvRzh1Bw7dgxLlixBXl4e6urqXG7z2muvQafTobi4GBcuXPByhJ4xWZ6/\n/PILHn/8caSlpWHfvn0+iNAzJsvzs88+Q1FREYqKivDEE0/g4sWLPojSf4n5PASLicbnW415tbW1\n0Ol0yM/Px3fffed8/KeffkJhYSHy8vLw+uuvez2X6TB2LsJ1Afr7+1FRUYH8/HwUFBSgpaUl5OtS\nW1vr/Axt2rQJgiCEbE1czYM8WQtBELBhwwbodDo89thjuHr16uRBUZCorq6muro6IiKqra2lnTt3\nTrjtvn37aNOmTfTMM894KzyPEZvn4OAgERFZrVYqKyujkydPei1GTxGTa2dnJ50/f56IiAYGBkin\n09GlS5e8GudUiX1Nf/zxRzp//jwtW7bMm+FNic1mo5ycHOro6CBBEKioqGjc69PY2Ehr1qwhIqIz\nZ85QWVmZL0KdEjF5Xrt2jVpbW+mNN96gvXv3+ijSqRGT5+nTp8loNBIRUVNTU0C+ntNFTP2CyUTj\n80Rj3s8//0zFxcU0PDxM7e3tlJOTQ3a7nYiIVqxYQS0tLURE9PTTT9OxY8d8kJFnjZ2LcF2IXnzx\nRfrkk0+IiGh4eJiMRmNI16Wjo4OysrLIYrEQEdH69evpwIEDIVsTV/MgT9bigw8+oFdeeYWIiI4c\nOUKVlZWTxhQ0R6QaGhpQWloKACgtLcXXX3/tcju9Xo+mpiaUlZV5MzyPEZtnREQEAEd3bbfboVar\nvRajp4jJNSEhAXPnzgUAREVF4c4770RnZ6dX45wqsa/pggULEBMT483Qpuzs2bNISUnBzJkzIZfL\nUVBQgIaGhlHbNDQ0oKSkBAAwf/589Pf3o7u72xfh3jYxecbHx+Oee+6BTCbzUZRTJybPe++9FyqV\nyvm7wWDwRah+SUz9gomr8dlgMEw45h09ehRLly6FTCbDrFmzkJKSgrNnz6Krqwsmkwnz5s0DAJSU\nlEw4TgYKV3ORUK/LwMAATp48ieXLlwMAZDIZVCpVSNclOjoacrkcQ0NDsFqtMJvN0Gq1IVsTV/Mg\nT9bi5v+Vl5eH77//ftKYgqaR6unpgUajAeAYvHt6elxut2PHDlRVVSEsLMyb4XmM2DztdjtKSkqw\nePFipKenIzU11ZtheoTYXEd0dHSgra3N+eEIFO7mGUgMBgOSk5Od97Va7bhGt7OzE0lJSaO2CbTJ\nt5g8g4G7eX788cfIyMjwRmgBIVTeJ66MjM/z58/HtWvXXI55rupjMBhgMBgCfowYy9VcJNTr0tHR\ngbi4OLz00ksoLS3Fli1bMDQ0FNJ1UavVKC8vx8MPP4yMjAyoVCosWrQopGsy1kRzqNupxc3zEalU\nipiYGPT29t7y+QPqq9HVq1e7/Ka6srJy3GOuGqXGxkZoNBrMnTsXzc3N0xKjJ0w1TwCQSCQ4dOgQ\nBgYGUF5ejhMnTiA9Pd3jsU6VJ3IFAJPJhIqKCmzevBlRUVEejdETPJUnY4Hihx9+wIEDB/Dhhx/6\nOhTmY2PH57FjXKiNeWLnIqFWF6vVivPnz2Pr1q1IS0vDjh07UFdXF9Lvl/b2dtTX1+Obb76BSqXC\n+vXrcfjw4ZCuyWQ8WQsimnSbgGqkbnWC9owZM9Dd3Q2NRoOuri7Ex8eP2+bUqVM4evQompqaYLFY\nYDKZUFVVherq6ukM221TzfNm0dHRyMzMxLlz5/yykfJErlarFRUVFSguLkZOTs50hTolnnxNA4lW\nqx11sqbBYEBiYuKobRITE6HX65339Xo9tFqt12L0BDF5BgOxeba1tWHr1q3Ys2dPQC4rni6h8j65\nmavxeaIxT6vV4vfff3f+7chYMPZxg8EQcGPEzVzNRV544QVoNJqQrktSUhKSkpKQlpYGANDpdNi9\ne3dIv19aW1tx3333ITY2FgCQk5OD06dPh3RNxvJkLUbmI1qtFjabDQMDA87aTyRolvZlZWXhwIED\nAICDBw8iOzt73DYbN25EY2MjGhoaUFNTg4ULF/pdEzUZMXn29PQ4r1piNptx/Phx5zr1QCImV8Bx\nFZfU1FSsWrXKm+F5jNg8AXHfjviTtLQ0XLlyBb/99hsEQcCRI0fG5ZednY1Dhw4BAM6cOYOYmBjn\nYfpAISbPmwXa6zhCTJ5Xr15FRUUFqqurcccdd/goUv/k7vskGLganyca87KysvD5559DEAS0t7fj\nypUrmDdvHhISEqBSqXD27FkQEQ4dOhTQdXM1F9m5cyceeeSRkK6LRqNBcnIyLl++DMBxVDs1NTWk\n3y+zZ89GS0sLLBYLiIhrgvH7T0/WIisrCwcPHgQAfPnll3jwwQdFBRQUrl+/TqtWrSKdTkerV6+m\nvr4+IiIyGAy0du3acds3NzcH5FX7xOTZ1tZGJSUlVFxcTIWFhbRnzx5fhnzbxOR68uRJmjNnDhUV\nFVFxcTGVlJRQU1OTL8N2m9j37saNG2nx4sV09913U2ZmpvPKRv6uqamJdDod5ebmUm1tLRER7d+/\nnz766CPnNq+++irl5ORQYWEhnTt3zlehTslkeXZ1dVFGRgbdf//99MADD1BmZiYNDAz4MuTbMlme\nL7/8MqWnpzvHoOXLl/syXL/jqn7BaqLxeaIxj4jo7bffppycHFqyZAl9++23zsdbW1tp2bJllJub\nS9u3b/dFOtPi5rkI14XowoUL9Oijj1JRURE9++yzZDQaQ74uu3fvpqVLl9KyZcuoqqqKBEEI2Zq4\nmgf19vZ6rBYWi4UqKiooNzeXysrKqL29fdKYwogC9KtRxhhjjDHGGPORoFnaxxhjjDHGGGPewo0U\nY4wxxhhjjLmJGynGGGOMMcYYcxM3UowxxhhjjDHmJm6kGGOMMcYYY8xN3EgxxhhjjDHGmJu4kWKM\nMcYYY4wxN3EjxRhjjDHGGGNu+n9ZtQimbnm6ugAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Age effect size estimates. Positive values suggest higher probability of event with each year above age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['θ_age'], ylabels=plot_labels[:-1])",
      "execution_count": 60,
      "outputs": [
        {
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408ea2240>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408d32f98>",
            "image/png": 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V+fPnlZKSosTERPXp00cOh0OSVK1atYvBScYYSdKuXbsUEBCg6tWry8PDQxEREfr6668l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\nAACsImwAAACrCBsAAMAqwgYAALDq/wGRnxg9Kze4fgAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_6_40'], ylabels=plot_labels)",
      "execution_count": 61,
      "outputs": [
        {
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408d58f60>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408cf8550>",
            "image/png": 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v1u/YsUOBgYGqUKGCPDw8FBISom+//VaSVKZMGbVs2VKSVK9ePTVv3lweHh6qX7++kpOT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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 12-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_12_40'], ylabels=plot_labels)",
      "execution_count": 62,
      "outputs": [
        {
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408ce5c88>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408ce5940>",
            "image/png": 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hISpXrpx8fHwUERGh7777TpJUqlQphYWFSZLq1KmjZs2aycfHR3Xr1lVycrKrjvXr1ys3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\nSUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_24_40'], ylabels=plot_labels)",
      "execution_count": 63,
      "outputs": [
        {
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408cdd630>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408cd26a0>",
            "image/png": 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Dg1W+fHn5+PgoIiJC3377rSSpVKlSCg0NlSTVrVtXzZs3l4+Pj+rVq6eUlBRXHevWrVN+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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_6_30'], ylabels=plot_labels)",
      "execution_count": 64,
      "outputs": [
        {
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408bd4390>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408bd4780>",
            "image/png": 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lZbmXs7KyVK1aNfdyhQoV1Lp1a7300ksaPHiwVq1addnHvBzr1knSHkl7lJ9/\nehkAgLKtTISN06MYPXv21FNPPaVbb731vOs2bdpU7du316xZs4rcX7lyZVWqVElbt26VdOoCz+K0\natVK//znP/Xbb79JOvWpl1atWkmS/vWvf+nQoUOSpMLCQu3YsUN169a9+OZKUJs2kvM/Y1FO56ll\nAADKujJxGuXMUxcPP/xwsesPHDhQvXv3Vv/+/YvcP3bsWA0fPlz+/v664447ir2gs23bttq+fbu6\nd+8up9Op6667TqNGjZIk/fvf/9bw4cPdQaRZs2Z66KGHLqW9EhMWdurUybp1p4IGp1AAAL6gXE0x\nf/LkSffHU6dNm6YjR47o5Zdf9nJVAABc2crEyEZJWbt2raZNm6aCggLVrVtX48aN83ZJAABc8crV\nyAYAACh7ysQFovAcc6MAAHwNYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVhAwAAWMVHXwEAgFWMbAAA\nAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbPoa5UQAAvoawAQAArCJsAAAAqwgbAADAKsIGAACw\nirABAACsYm4UAABgFSMbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwoaPYW4UAICvIWwAAACr\nCBsAAMAqwgYAALCKsAEAAKwibAAAAKuYGwUAAFjFyAYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAA\nsIqw4WOYGwUA4GsIGwAAwCqfCRshISHnvD82NlaffvrpBbeNj4/X4cOH3cuvvvqqdu3aVaL1AQCA\nc/OZsOFwOC5524ULFyo9Pd29PHr0aN18880lURYAAChGmQwbQ4YMUY8ePRQVFaV58+ZJkowxGjdu\nnCIjI/Xoo4/q6NGjZ203depU9erVS1FRURoxYoQkaeXKldq+fbuGDh0ql8ul3Nxc9e3bVz/88IMk\naenSpYqKilJUVJQmTJjg3ldISIgmTZqkmJgY9enTRxkZGaXQefFyc6WsLCkpyduVAADgmTIZNsaN\nG6cFCxZo/vz5mjVrljIzM5WTk6NmzZpp6dKlCg0N1dSpU8/arm/fvpo3b56WLFmiX3/9VWvXrlXn\nzp3VtGlTTZw4UfHx8QoMDHSvf+jQIU2cOFGzZ89WQkKCtm3bpsTERElSTk6OWrRooYSEBLVs2VKf\nfPJJqfV/PklJ0sGDUmam1Lo1gQMA4BvKZNiYOXOmYmJi1Lt3bx08eFC//PKL/P391aVLF0lSdHS0\nvv3227OKuwAJAAAQDklEQVS227Bhg3r37q2oqCht3LhRP/30k/uxc00Bs23bNrVq1UpVq1aVn5+f\noqKi9M0330iSKlSooDZt2kiSmjRpotTUVButXpR16yRpj6Q9ys8/vQwAQNnm9HYBv7dp0yYlJSVp\n3rx5CggIUN++fZWbm3vWer+/hiMvL0+vv/66Fi5cqODgYMXFxZ1zu9873zx0Tud/nxp/f3/l5+df\nZCclr00byemU8vNP/f2fLAQAQJlW5kY2jh8/ripVqiggIEC7du3Sli1bJEkFBQVasWKFJGnJkiVq\n0aJFke1yc3PlcDhUrVo1ZWdna+XKle7HKlWqpBMnTpx1rGbNmunrr79WZmamCgoKtGzZMt15550W\nu7s8YWHS+vXS+PGn/g4L83ZFAAAUr8yNbLRu3Vpz585VRESEbrzxRvdHXitWrKht27bp3XffVY0a\nNTRp0qQi21WuXFk9e/ZURESEatasqdtuu839WPfu3fXaa68pKChIc+fOdY+K1KxZUy+++KL69u0r\nSWrbtq3atWsn6fI+/WJTWBghAwDgWxzmfOcRAAAASkCZO40CAADKF8KGj2FuFACAryFsAAAAqwgb\nAADAKsIGAACwirABAACsImwAAACr+J4NAABgFSMbAADAKsIGAACwirABAACsImwAAACrCBsAAMAq\nwoaPYW4UAICvIWwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKuYGwUAAFjFyAYAALCKsAEAAKwi\nbAAAAKsIGwAAwCrCBgAAsIqw4WOYGwUA4GsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKuZG\nAQAAVjGyAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImz4GOZGAQD4GsIGAACw6ooMGw888MBF\nrb9p0yY98cQTlqoBAKB8uyLDxkcffeTtEgAAuGJckWEjJCRE0tkjFqNHj9aiRYskSZ9//rm6dOmi\n7t2769NPP/VKneeSmytlZUlJSd6uBAAAz1yRYcPhcFzw8by8PI0YMULTpk3TwoULdeTIkVKq7MKS\nkqSDB6XMTKl1awIHAMA3XJFhozg///yzrrvuOl133XWSpOjoaC9XdMq6dZK0R9Ie5eefXgYAoGy7\nosOGv7+/zpyHLjc31327LM5P16aN5HSeuu10nloGAKCsuyLDxukgUbduXe3cuVO//fabjh07pg0b\nNkiSbrrpJqWlpWnfvn2SpGXLlnmt1jOFhUnr10vjx5/6OyzM2xUBAFA8p7cL8IbT12zUrl1bXbp0\nUWRkpOrVq6cmTZpIkgICAjRq1CgNGjRIQUFBCg0NVXZ2tjdLdgsLI2QAAHyLw5TF8wUAAKDcuCJP\nowAAgNJD2PAxzI0CAPA1hA0AAGAVYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBXfswEAAKxiZAMAAFhF\n2AAAAFYRNgAAgFWEDQAAYBVhAwAAWEXY8DHMjQIA8DWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQN\nAABgFXOjAAAAqxjZAAAAVhE2AACAVYQNAABgFWEDAABYRdgAAABWETZ8DHOjAAB8DWEDAABYRdgA\nAABWETYAAIBVhA0AAGAVYQMAAFjF3CgAAMAqRjYAAIBVhA0AAGAVYQMAAFhF2AAAAFYRNgAAgFWE\nDR/D3CgAAF9TamEjPj5ehw8fLrH9bdq0SZs3by6x/Xn7OAAAlFelFjYWLlyo9PT0cz5WWFh40fsj\nbAAA4Bs8+lKv1NRUPf7442rZsqU2b96s4OBgvfvuu9q1a5dGjhypX3/9Vddff73eeOMNVa5c+azt\nV65cqWHDhql27dq66qqrNHfuXHXp0kVdu3bVV199pYEDB+q2227TqFGjdPToUQUFBWn06NG68cYb\ntWbNGr377rvKz89X1apVNWHCBOXk5Oj++++Xv7+/qlevruHDh2v+/PkKDAxUcnKyMjIyNGbMGMXH\nx2vr1q1q3ry5xo0bJ0n68ssvNWXKFOXl5en666/XuHHjFBQUpPbt28vlcmnNmjXKz8/X5MmTFRAQ\ncNZxWrZsWfKvwkU4fQplz549Xq0DAACPGQ/s37/fNGnSxKSkpBhjjHnuuedMQkKCiYqKMl9//bUx\nxpjJkyebsWPHnncfffv2NT/88IN7uV27duZvf/ube7l///7ml19+McYYs2XLFtOvXz9jjDHHjh1z\nr/PJJ5+Y8ePHG2OMmTJlipkxY4b7sWHDhpkXXnjBGGPMqlWrTEhIiPnpp5+MMca4XC6TnJxsMjIy\nzEMPPWRycnKMMcZMmzbNTJ061V3Phx9+aIwxZs6cOWb48OHnPI631a9f39SvX9/bZQBAubFhgzHj\nx5/6G3Y4PQ0ldevWVcOGDSVJjRs31t69e3XixAmFhoZKklwul5599tkLhRqZ3w2idO3aVZJ08uRJ\nbd68Wc8++6x7nfz8fEnSgQMH9Nxzz+nQoUPKz89XvXr1znuMdu3aSZIaNGigmjVr6pZbbpEk3Xrr\nrUpNTdXBgwe1c+dOPfDAAzLGKD8/XyEhIe7tO3bsKElq2rSpVq1a5elTAwAoRRER0vLl3q6ieF27\nSsuWebuKssHjsBEQEOC+7e/vr+PHj1/2wYOCgiSdumajSpUqio+PP2ud0aNH67HHHlPbtm21adMm\nxcXFFVujn59fkXr9/PxUUFAgPz8/3X333Zo4cWKx258OO2UNp08AeEPTptIPP3i7Ct+yfLnkcHi7\nivNr0kTavr10jnXJF4hWrlxZVapU0bfffitJSkhI0J133nne9a+++mqdOHHivI/Vq1dPK1ascN+X\nkpIiScrOzlatWrUkqUgYqVSp0nn3dz7NmzfX5s2btXfvXklSTk5OsW/el3IcAChvtm+XjCl/fzZs\nkJz/+bXb6Ty17O2aSutPaQUN6TI/jTJ+/Hi99dZbiomJUUpKioYMGXLedV0ul1577TW5XC7l5ubK\n8bu4N2HCBM2fP18xMTGKjIzU6tWrJUlDhgzRM888ox49eqh69eru9du1a6fPPvtMLpfLHXiKU716\ndY0bN04vvPCCoqOj1adPH+3evVuSzqrnco4DAPANYWHS+vXS+PGn/g4L83ZF5RNTzAMAAKv4BlEA\nAGCVxxeIeur111/Xd999J4fDIWOMHA6H+vXrJ5fLVdKHAgAAPoDTKD6GL/UCAPgaTqMAAACrCBsA\nAMAqwgYAALCKsAEAAKwibAAAAKv4NAoAALCKkQ0AAGAVYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVh\nw8fccMMN7vlRAADwBYQNAABgFWEDAABYRdgAAABWETYAAIBVTm8XUN7l5+fr4MGDJb7f/fv3l/g+\nAQC4HLVr15bTeXa0YG4Uy/bv368OHTp4uwwAAKxLTExUvXr1zrqfsGGZjZGNDh06KDExsUT3WRbR\nZ/lCn+ULfZYfJdnj+UY2OI1imdPpPGfKu1w29lkW0Wf5Qp/lC32WH7Z75AJRAABgFWEDAABYRdgA\nAABW+Y8cOXKkt4vAxWvVqpW3SygV9Fm+0Gf5Qp/lh+0e+TQKAACwitMoAADAKsIGAACwirABAACs\nImwAAACrCBsAAMAqwgYAALCKsFGGff7557rvvvvUuXNnTZs27ZzrjBkzRp06dVJMTIySk5NLucKS\nUVyfP//8s/r06aPbbrtNH3zwgRcqvHzF9bhkyRJFR0crOjpaDzzwgHbs2OGFKi9fcX0mJiYqOjpa\n3bp1U/fu3bVhwwYvVHn5PPm/KUlbt25VkyZN9Omnn5ZidSWnuD43bdqk0NBQuVwuuVwu/fWvf/VC\nlZfPk9dz48aN6tatmyIjI9W3b99SrrBkFNfn9OnT1a1bN7lcLkVFRalx48Y6duxYyRzcoEwqKCgw\n9957r9m/f7/Jy8sz0dHRZufOnUXWWbt2rXn88ceNMcZ8//33plevXt4o9bJ40ue///1vs23bNjNp\n0iQzY8YML1V66TzpcfPmzebYsWPGGGPWrVtXbl/LkydPum+npKSYe++9t7TLvGye9Hl6vX79+plB\ngwaZlStXeqHSy+NJnxs3bjSDBw/2UoUlw5M+jx07Zrp27WoOHjxojDn1M8nXePrv9rTVq1eb/v37\nl9jxGdkoo7Zu3ar69eurbt26qlChgiIiIs6aAjgxMVHdunWTJDVv3lzHjx/XkSNHvFHuJfOkz+rV\nq6tp06bnnLbYF3jS4+23367KlSu7b6enp3uj1MviSZ9BQUHu2ydPnlS1atVKu8zL5kmfkjR79mx1\n7txZ1atX90KVl8/TPn2dJ30uWbJEnTp1UnBwsCT55Gt6sa/n0qVLFRERUWLHJ2yUUenp6apTp457\nOTg4WIcOHSqyzqFDh1S7du0i6/jam5Qnffq6i+1x3rx5uueee0qjtBLlaZ+rVq1Sly5dNGjQIA0f\nPrw0SywRnvSZnp6uVatW6cEHHyzt8kqMp6/n5s2bFRMTo0GDBmnnzp2lWWKJ8KTPPXv2KCsrS337\n9lWPHj20aNGi0i7zsl3Mz6Fff/1VX3zxhTp37lxix/fNXxWBciopKUkLFy7UP/7xD2+XYs29996r\ne++9V998842GDh2qlStXerukEvfGG29o6NCh7mVTTmeFaNKkidauXaugoCCtW7dOQ4YMKZevZ0FB\ngf71r39p5syZOnnypPr06aOQkBDVr1/f26VZsXr1arVo0UJVqlQpsX0SNsqo4OBgpaWluZfT09NV\nq1atIuvUqlVLBw8edC8fPHjQPcznKzzp09d52mNKSopGjBihv/3tb7rmmmtKs8QScbGvZWhoqAoK\nCnT06FGfOp3iSZ/bt2/X888/L2OMjh49qs8//1xOp1MdOnQo7XIvmSd9VqpUyX27TZs2GjVqlDIz\nM1W1atVSq/NyedJncHCwqlWrpsDAQAUGBio0NFQpKSk+FTYu5v/n8uXLFRkZWaLH5zRKGXXbbbdp\n7969Sk1NVV5enpYtW3bWD6oOHTq4h/O+//57ValSRddee603yr1knvR5Jl/8DdGTHtPS0vTMM8/o\nrbfe0vXXX++lSi+PJ33u3bvXffuHH36QJJ8KGpJnfSYmJioxMVGrV6/Wfffdp9dee82ngobkWZ9n\nXiO2detWSfKpoCF5/rP222+/VUFBgXJycrR161bdfPPNXqr40nj6s/b48eP6+uuvS/zfKyMbZZS/\nv79effVVDRgwQMYY9ezZUzfffLPmzp0rh8Oh+++/X23atNG6devUsWNHBQUFady4cd4u+6J50ueR\nI0fUo0cPZWdny8/PT7NmzdKyZcuK/FZVlnnS41//+ldlZWVp1KhRMsbI6XRq/vz53i79onjS58qV\nK5WQkKAKFSooKChIkyZN8nbZF82TPssDT1/Pjz76SE6nU1dddVW5fT1vvvlmhYeHKzo6Wn5+furd\nu7duueUWb5d+UTz9d7tq1SqFh4frqquuKtHjM8U8AACwitMoAADAKsIGAACwirABAACsImwAAACr\nCBsAAMAqwgYAALCKsAEAAKz6fwDb83PFZiT0AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_6_50'], ylabels=plot_labels)",
      "execution_count": 65,
      "outputs": [
        {
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408bbe630>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408ae7780>",
            "image/png": 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lypWTl5eXwsPD9f3330uSSpUqpZCQEElSvXr11KJFC3l5eal+/fpKSUlx1bF+/Xrl5eVp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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_24_30'], ylabels=plot_labels)",
      "execution_count": 66,
      "outputs": [
        {
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408ace2b0>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408c4a7b8>",
            "image/png": 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lJk2aqGzZsvLx8VFERIS2bt0qSSpRooRCQ0MlSTVr1lTjxo3l4+OjWrVqKSkpyVXHunXr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\nTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_uae, varnames=['p_24_50'], ylabels=plot_labels)",
      "execution_count": 67,
      "outputs": [
        {
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc408d48dd8>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc408bf4f28>",
            "image/png": 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Jtc9du3YpKChIFStWlCRFRETo22+/VWhoqMqUKaOQkBBJUoMGDeTr6ysvLy81bNhQKSkp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\nYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "def generate_table(model, trace):\n    \n    tables = []\n\n    for scenario in uae_model.deterministics[1:]:\n\n        table = pm.df_summary(trace, varnames=[scenario]).round(3).drop('mc_error', axis=1)\n        table.index = plot_labels.values.tolist()[:-1] + ['none']\n\n        tokens = scenario.name.split('_')\n        fup = int(tokens[1])\n        age = int(tokens[2])\n\n        table['age'] = age\n        table['followup'] = fup\n        table.index.name = 'next intervention'\n\n        tables.append(table)\n        \n    return (pd.concat(tables).set_index(['age', 'followup'], append=True)\n                 .reorder_levels([1,2,0])\n                 .sort_index(level='age'))\n",
      "execution_count": 68,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "uae_table = generate_table(uae_model, trace_uae)",
      "execution_count": 69,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "uae_table",
      "execution_count": 80,
      "outputs": [
        {
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "                                 mean     sd  hpd_2.5  hpd_97.5\nage followup next intervention                                 \n30  6        MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.000\n             hysterectomy       0.002  0.002    0.000     0.006\n             iud                0.000  0.000    0.000     0.000\n             myomectomy         0.397  0.053    0.292     0.498\n             none               0.591  0.052    0.493     0.696\n             uae                0.010  0.008    0.000     0.025\n    12       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.000\n             hysterectomy       0.003  0.002    0.000     0.008\n             iud                0.000  0.000    0.000     0.000\n             myomectomy         0.385  0.052    0.285     0.485\n             none               0.601  0.051    0.499     0.698\n             uae                0.011  0.009    0.000     0.029\n    24       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.000\n             hysterectomy       0.005  0.004    0.000     0.013\n             iud                0.000  0.000    0.000     0.000\n             myomectomy         0.361  0.066    0.227     0.484\n             none               0.618  0.064    0.494     0.744\n             uae                0.015  0.012    0.001     0.041\n40  6        MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.000\n             hysterectomy       0.018  0.005    0.008     0.027\n             iud                0.000  0.000    0.000     0.000\n             myomectomy         0.035  0.008    0.022     0.053\n             none               0.933  0.011    0.913     0.954\n             uae                0.014  0.005    0.005     0.023\n    12       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.001\n...                               ...    ...      ...       ...\n             none               0.929  0.010    0.908     0.948\n             uae                0.016  0.005    0.007     0.025\n    24       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.000  0.000    0.000     0.001\n             hysterectomy       0.035  0.009    0.017     0.050\n             iud                0.000  0.000    0.000     0.000\n             myomectomy         0.029  0.006    0.019     0.042\n             none               0.915  0.012    0.893     0.939\n             uae                0.021  0.005    0.010     0.031\n50  6        MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.039  0.085    0.000     0.212\n             hysterectomy       0.067  0.039    0.003     0.139\n             iud                0.342  0.270    0.002     0.857\n             myomectomy         0.001  0.001    0.000     0.003\n             none               0.542  0.234    0.095     0.872\n             uae                0.010  0.007    0.000     0.023\n    12       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.043  0.083    0.000     0.214\n             hysterectomy       0.091  0.043    0.017     0.179\n             iud                0.265  0.203    0.003     0.685\n             myomectomy         0.001  0.001    0.000     0.003\n             none               0.588  0.176    0.219     0.858\n             uae                0.012  0.007    0.002     0.026\n    24       MRIgFUS            0.000  0.000    0.000     0.000\n             ablation           0.050  0.083    0.000     0.218\n             hysterectomy       0.151  0.057    0.055     0.275\n             iud                0.154  0.123    0.001     0.404\n             myomectomy         0.001  0.001    0.000     0.002\n             none               0.627  0.110    0.419     0.832\n             uae                0.018  0.009    0.004     0.034\n\n[63 rows x 4 columns]",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>mean</th>\n      <th>sd</th>\n      <th>hpd_2.5</th>\n      <th>hpd_97.5</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th>followup</th>\n      <th>next intervention</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"21\" valign=\"top\">30</th>\n      <th rowspan=\"7\" valign=\"top\">6</th>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.002</td>\n      <td>0.002</td>\n      <td>0.000</td>\n      <td>0.006</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.397</td>\n      <td>0.053</td>\n      <td>0.292</td>\n      <td>0.498</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.591</td>\n      <td>0.052</td>\n      <td>0.493</td>\n      <td>0.696</td>\n    </tr>\n    <tr>\n      <th>uae</th>\n      <td>0.010</td>\n      <td>0.008</td>\n      <td>0.000</td>\n      <td>0.025</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">12</th>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.003</td>\n      <td>0.002</td>\n      <td>0.000</td>\n      <td>0.008</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.385</td>\n      <td>0.052</td>\n      <td>0.285</td>\n      <td>0.485</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.601</td>\n      <td>0.051</td>\n      <td>0.499</td>\n      <td>0.698</td>\n    </tr>\n    <tr>\n      <th>uae</th>\n      <td>0.011</td>\n      <td>0.009</td>\n      <td>0.000</td>\n      <td>0.029</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">24</th>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.005</td>\n      <td>0.004</td>\n      <td>0.000</td>\n      <td>0.013</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.361</td>\n      <td>0.066</td>\n      <td>0.227</td>\n      <td>0.484</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.618</td>\n      <td>0.064</td>\n      <td>0.494</td>\n      <td>0.744</td>\n    </tr>\n    <tr>\n      <th>uae</th>\n      <td>0.015</td>\n      <td>0.012</td>\n      <td>0.001</td>\n      <td>0.041</td>\n    </tr>\n    <tr>\n      <th rowspan=\"19\" valign=\"top\">40</th>\n      <th rowspan=\"7\" valign=\"top\">6</th>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.018</td>\n      <td>0.005</td>\n      <td>0.008</td>\n      <td>0.027</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.035</td>\n      <td>0.008</td>\n      <td>0.022</td>\n      <td>0.053</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.933</td>\n      <td>0.011</td>\n      <td>0.913</td>\n      <td>0.954</td>\n    </tr>\n    <tr>\n      <th>uae</th>\n      <td>0.014</td>\n      <td>0.005</td>\n      <td>0.005</td>\n      <td>0.023</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">12</th>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.001</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>0.929</td>\n      <td>0.010</td>\n      <td>0.908</td>\n      <td>0.948</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.016</td>\n      <td>0.005</td>\n      <td>0.007</td>\n      <td>0.025</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">12</th>\n      <th>uae</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>MRIgFUS</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.001</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.035</td>\n      <td>0.009</td>\n      <td>0.017</td>\n      <td>0.050</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.029</td>\n      <td>0.006</td>\n      <td>0.019</td>\n      <td>0.042</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.915</td>\n      <td>0.012</td>\n      <td>0.893</td>\n      <td>0.939</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.021</td>\n      <td>0.005</td>\n      <td>0.010</td>\n      <td>0.031</td>\n    </tr>\n    <tr>\n      <th rowspan=\"21\" valign=\"top\">40</th>\n      <th rowspan=\"7\" valign=\"top\">24</th>\n      <th>uae</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>MRIgFUS</th>\n      <td>0.039</td>\n      <td>0.085</td>\n      <td>0.000</td>\n      <td>0.212</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.067</td>\n      <td>0.039</td>\n      <td>0.003</td>\n      <td>0.139</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.342</td>\n      <td>0.270</td>\n      <td>0.002</td>\n      <td>0.857</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.001</td>\n      <td>0.001</td>\n      <td>0.000</td>\n      <td>0.003</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.542</td>\n      <td>0.234</td>\n      <td>0.095</td>\n      <td>0.872</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.010</td>\n      <td>0.007</td>\n      <td>0.000</td>\n      <td>0.023</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">6</th>\n      <th>uae</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>MRIgFUS</th>\n      <td>0.043</td>\n      <td>0.083</td>\n      <td>0.000</td>\n      <td>0.214</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.091</td>\n      <td>0.043</td>\n      <td>0.017</td>\n      <td>0.179</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.265</td>\n      <td>0.203</td>\n      <td>0.003</td>\n      <td>0.685</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.001</td>\n      <td>0.001</td>\n      <td>0.000</td>\n      <td>0.003</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.588</td>\n      <td>0.176</td>\n      <td>0.219</td>\n      <td>0.858</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.012</td>\n      <td>0.007</td>\n      <td>0.002</td>\n      <td>0.026</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">12</th>\n      <th>uae</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>MRIgFUS</th>\n      <td>0.050</td>\n      <td>0.083</td>\n      <td>0.000</td>\n      <td>0.218</td>\n    </tr>\n    <tr>\n      <th>ablation</th>\n      <td>0.151</td>\n      <td>0.057</td>\n      <td>0.055</td>\n      <td>0.275</td>\n    </tr>\n    <tr>\n      <th>hysterectomy</th>\n      <td>0.154</td>\n      <td>0.123</td>\n      <td>0.001</td>\n      <td>0.404</td>\n    </tr>\n    <tr>\n      <th>iud</th>\n      <td>0.001</td>\n      <td>0.001</td>\n      <td>0.000</td>\n      <td>0.002</td>\n    </tr>\n    <tr>\n      <th>myomectomy</th>\n      <td>0.627</td>\n      <td>0.110</td>\n      <td>0.419</td>\n      <td>0.832</td>\n    </tr>\n    <tr>\n      <th>none</th>\n      <td>0.018</td>\n      <td>0.009</td>\n      <td>0.004</td>\n      <td>0.034</td>\n    </tr>\n  </tbody>\n</table>\n<p>63 rows × 4 columns</p>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "def factorplot(table):\n    \n    table_flat = table.reset_index().rename(columns = {'mean':'probability'})\n    \n    sns.factorplot(x=\"followup\", y=\"probability\", hue=\"next intervention\", col=\"age\", \n                   data=table_flat[table_flat['next intervention']!='none'], \n                   facet_kws={'ylim':(0,0.6)})",
      "execution_count": 76,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "factorplot(uae_table)",
      "execution_count": 77,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4085e90f0>",
            "image/png": 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brKwsnn76aZo2bUp0dDQTJ04kOTmZr7/+GoPBQJcuXXjyySdJSUlhypQpBAUF4enpyb59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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "uae_table_flat = uae_table.reset_index().rename(columns = {'mean':'probability'}).drop(['sd'],\n                                                                                      axis=1)",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Myomectomy model"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "myomectomy_model = specify_model(pm.Model(), 'myomectomy')",
      "execution_count": 81,
      "outputs": [
        {
          "name": "stdout",
          "text": "Applied log-transform to σ and added transformed σ_log_ to model.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "with myomectomy_model:\n    \n    trace_myomectomy = pm.sample(n_iterations, step=pm.Metropolis(), random_seed=20140925)",
      "execution_count": 82,
      "outputs": [
        {
          "name": "stdout",
          "text": " [-----------------100%-----------------] 100000 of 100000 complete in 140.6 sec",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Baseline estimates on log scale"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['μ'], ylabels=plot_labels)",
      "execution_count": 85,
      "outputs": [
        {
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc3fdd5ce80>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc3fdd5cd30>",
            "image/png": 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T2rVrp1KlSik4OFiSVK9ePZUuXVoeHh6qX7++kpOTnXVMmzZNf/7zn7VgwQJFRUUVMr4B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\n/wdMfxuD+wPpqgAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Followup time effect"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['θ_fup'], ylabels=plot_labels[:-1])",
      "execution_count": 86,
      "outputs": [
        {
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc3fdd67ef0>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc431e6be48>",
            "image/png": 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kHA6HJKlq1aqXgpOMMZKk3bt3KzAwUNWrV5eHh4ciIiK0fft2SZKXl5eCg4MlSY0aNVLr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\nKsIGAACwirABAACs+j8aD61m7+MFyAAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Age over 40 effect"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['θ_age'], ylabels=plot_labels[:-1])",
      "execution_count": 87,
      "outputs": [
        {
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc431e632e8>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc431e63128>",
            "image/png": 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tmj9/vnr06KF+/frp3LlzSk9PV2Jioh555BE5HA5JUpUqVS4EJxljJEl79+5VQECAqlat\nKh8fH0VERGjXrl2SpPLlyysoKEiS1KRJE7Vr104+Pj5q2rSp0tLSXHV8/vnnKigo0KpVqxQdHX1t\nKQ6AJKlFC8nhkL78sujzX355/nmH4/w2AG5dzpJs5Ovr63pcrlw5nT17Vl27dtXs2bMVGBioFi1a\n6Pbbb5ckrVixQtu2bdPGjRu1dOlSLV68uMixCgsLVaVKFcXHxxd7rooVKxZZnj17dqk/efBC8Pg1\np/Pfl+3j4+O6PofD4Ror8pvf/Ebt27dXQkKCNm7cqNWrV5eqBsDbJSWd/56YKAUHS/n5ktMpbdki\nBQa6tzYAN0epB4j6+voqODhY48ePd43XOHPmjE6dOqUOHTooJibGNcahUqVKOn36tCTptttuU/36\n9bVx40bXsS4eG3GxoKAgLVmyxLWcnJwsSWrfvr2WL1/uCgYnT550HfvCeVq2bKmdO3cqOztbBQUF\nWr9+vdq1a3fV67o4oPTu3VsTJ05Uy5YtVbly5ZI1DIBiBQaeDxhTphA0AG9Top6Ny4mIiFBCQoLr\nlkROTo6eeeYZ19tPY2JiJEmhoaF65ZVXtHTpUs2aNUszZszQq6++qrlz56qgoEChoaFq1qzZJcd/\n5plnNGnSJNdbb+vVq6d58+apT58+SklJUWRkpMqXL68+ffroscceU9++fTVo0CD5+/tr8eLFevHF\nF9WvXz9JUqdOndS5c2dJct1+Kc7F65o3b67bbrutyGBYANfuQu9kSkoKIQPwQg5zuXsNJbBw4UKd\nPn1aw4YNu5E1eYyMjAwNGDCgSC8MgGt3cdgA4H1K3bPx7LPPKjU19ZIxGbeKDz74QLNmzXL1zgAA\ngNK5rp4NACgJejYA78bcKAAAwCrCBgAAsIrbKAAAwCp6NgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2\nAACAVYQNANY1bNiw1LM3Ayj7CBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrmRgEAAFbRswEA\nAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsALCOuVEA70bYAAAAVhE2AACAVYQNAABgFWEDAABY\nRdgAAABWMTcKAACwip4NAABgFWEDAABYRdgAAABWETYAAIBVhA0AAGAVYQOAdcyNAng3wgYAALCK\nsAEAAKwibAAAAKsIGwAAwCrCBgAAsIq5UQAAgFX0bAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAA\nqwgbAKxjbhTAu91SYWP//v36/PPP3V0GAAC4yC0VNpKTk/XFF1+4uwwAAHARZ0k2SktL06BBg/Tb\n3/5WX331lVq0aKGePXtq9uzZysrK0vTp0zVy5EgtX75c1apVkzFG3bp103vvvaczZ85o9OjRys7O\nVvXq1TV58mTVqVNHMTExqlChgpKTk5WZmamJEycqPj5ee/bs0X333afJkydLkv72t79p9uzZysvL\n05133qnJkyfLz89Pe/bs0euvv67c3FxVqFBBCxcu1J/+9CedPXtWX331lQYPHqz27dtr9OjRSk1N\nVcWKFfXaa6+pSZMmiouL09GjR5Wamqpjx45p1KhR2r17t7Zu3ao6depo3rx52rlzp5YsWaI5c+ZI\nkr788kv93//9n+Li4uz9awBlUGKi9PnnUseOUmCgu6sB4JFMCRw9etQ0b97cfPfdd8YYY6Kjo01M\nTIwxxpjNmzebZ555xsTFxZk///nPxhhjtm7dap577jljjDFPP/20+eCDD4wxxqxcudI888wzxhhj\nRo0aZV544QVjjDEJCQmmVatWRY6fnJxsMjMzzWOPPWZyc3ONMcbMnz/fzJkzx+Tl5ZkuXbqYpKQk\nY4wxp0+fNvn5+Wb16tUmNjbWVXdsbKyJi4szxhizbds2ExUVZYwxZvbs2ebRRx81BQUFJjk52bRs\n2dJs2bLFGGPM0KFDTUJCgjHGmO7du5vMzExjjDEvvPCC+fTTT0vSXMAtLTTUGKn4r/bti9+nQYMG\npkGDBje1TgCeo8S3UerVq6e7775bktS4cWO1b9/e9Tg9PV29e/fWmjVrJEmrVq1Sr169JElff/21\nwsPDJUlRUVH66quvXMfs3LmzJKlJkyaqVatWkeOnpaXpm2++0cGDB/WHP/xBPXr00Jo1a5Senq7D\nhw+rdu3aat68uSSpUqVKKleu3CU1//3vf1dUVJQkKTAwUCdPnlROTo4kqUOHDvLx8VHTpk1ljFFQ\nUJCrlrS0NFe9a9eu1alTp/TNN9+oQ4cOJW0uoMxp0UJyOK7+tWHD5Y/x5ZeXbt+ixc27BgCeqUS3\nUSTJ19fX9djHx8e17OPjo/z8fPn7+6tmzZpKTEzU3r179cYbb0iSHA7HVY958fEuLBcUFMjHx0cP\nPPCA61gXfPvttzIlmNKlJOd2OBxyOv/dDBfOLUnR0dEaMmSIfH199fDDD8vH55Ya4gIUkZR07fsk\nJkrBwVJ+vuR0Slu2XO5WSsp1VgegLLuhfz179+6tkSNHqnv37q4/9K1atdK6deskSWvXrlXbtm1L\nfLz77rtPu3fv1pEjRyRJubm5SklJUaNGjXTixAkl/evVMScnRwUFBapUqZJOnz7t2r9NmzZau3at\nJGn79u2qVq2aKlWqdMl5Lhdcateurdq1a2vevHnq2bNniesGvEVg4PmAMWXKlYIGAG9X4p6NkggJ\nCdHo0aMVHR3tem7s2LGKiYnRwoULXQNES+rC9i+88ILy8vLkcDg0fPhwNWzYUDNnzlRsbKx++eUX\n+fn5adGiRQoICND8+fMVHR2twYMH67nnnlNMTIwiIyNVsWJFTZ06tdjzXKkHJDIyUtnZ2brrrrtK\n3hCAFwkMJGQAuLIbOsX83r17NXXqVC1duvRGHdLtYmNjdc8997jGoAAAgGtzw3o25s+fr+XLl18y\nvqIs69mzpypVqqRRo0a5uxQAAMqsG9qzAQAA8Gu8vQKAdcyNAng3wgYAALCKsAEAAKwibAAAAKsI\nGwAAwCrCBgAAsIq3vgIAAKvo2QAAAFYRNgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AFjH3CiAdyNs\nAAAAqwgbAADAKsIGAACwirABAACsImwAAACrmBsFAABYRc8GAACwirABAACsImwAAACrCBsAAMAq\nwgYAALCKsAHAOuZGAbwbYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVhAwAAWMXcKAAAwCp6NgAAgFWE\nDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQNANYxNwrg3QgbAADAKsIGAACwirABAACsImwAAACrCBsA\nAMAq5kYBAABW0bMBAACsImwAAACrCBsAAMAqwgYAALDK6e4Cbpa0tDQNGTJEH374oSRp4cKFOnPm\njPz9/fXee+8pPz9fd955p6ZPn64KFSooMzNT48eP17FjxyRJMTExat26tTsvAfAoiYnS559LHTtK\ngYHurgaAJ/OasHE5Xbt2VZ8+fSRJb731llauXKnHHntMkyZN0hNPPKHWrVvr2LFjeuqpp7RhwwY3\nV4srCQuT+CfyVA3/9T3FjTXYFRoqrV/v7ioAz+T1YePbb7/VW2+9pZ9//lm5ubkKCgqSJG3btk3f\nf/+9Lrwz+MyZM8rNzZWfn587y5UktWgh7dvn7ioAXGzDBsnhcHcV+LXmzaWkJHdXAa8JG06nU4WF\nha7ls2fPSpJGjRqluXPnqkmTJoqPj9eOHTskScYYvf/++ypfvrxb6r0SfnHgbomJUnCwlJ8vOZ3S\nli1XvpVyYcLXlJSbUR0AT+M1A0Rr1KihzMxMnTx5Unl5efrss88kne+xqFmzps6dO+cazyFJDzzw\ngN59913X8v79+292yYDHCgw8HzCmTLl60AAAr/oE0aVLl2rx4sWqU6eO6tevr3r16qlmzZp65513\nVKNGDbVs2VI5OTmaPHmysrKy9Nprr+nQoUMqLCxU27ZtNX78eHdfAlAmNfxX10YKXRuAV/KqsAHA\nPQgbgHcjbAAAAKu8ZswGAABwD8IGAACwirABAACsImwAAACrCBsAAMAqwgYA6xo2bOh6+ysA70PY\nAAAAVhE2AACAVYQNAABgFWEDAABYRdgAAABWMTcKAACwip4NAABgFWEDAABYRdgAAABWETYAAIBV\nhA0AAGAVYQOAdcyNAng3wgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIq5UQAAgFX0bAAAAKsI\nGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAKxjbhTAuxE2AACAVYQNAABgFWEDAABYRdgAAABWETYA\nAIBVzI0CAACsomcDAABYRdgAAABWETYAAIBVhA0AAGAVYQMAAFhF2ABgHXOjAN6NsAEAAKwibAAA\nAKsIGwAAwCrCBgAAsIqwAQAArPKIsNGsWTO9/PLLruWCggIFBgZqyJAhkqT4+Hj97ne/U3R0tMLC\nwrRs2TLXtnFxcVq0aNEVj79jxw61bdtW0dHR6tGjhwYOHChJiomJ0UcffVRk21atWkmSjDGaOHGi\nIiIiFBERoT59+igtLe2GXC/gbVJSUpSSkuLuMgC4idPdBUiSn5+fvvvuO+Xl5cnX11d/+9vfVLdu\n3SLbhIWFaezYscrOzlZoaKi6d++u6tWrl/gcbdu21bx58666ncPhkCRt2LBBx48f14cffihJysjI\nUMWKFa/hqgAAgOQhPRuS1KFDB3322WeSpPXr1yssLKzY7apWrao77rhDR48evWTdnj17FBkZqejo\naE2bNk0RERGlruf48eOqVauWa9nf31+VK1cu9fEAnJeYKE2dev47AO/gEWHD4XAoLCxM69atU15e\nng4cOKD77ruv2G3T09N19OhR3XnnnZesGzNmjCZOnKj4+HiVK1euyLpdu3YpOjpa0dHRevvtt69a\nU/fu3fXJJ58oOjpaU6dOVXJycukuDvBAYWGSw+Ger9/9Tho16vx3d9XgcJxvAwA3h0fcRpGkJk2a\nKC0tTevWrVPHjh1ljCmyfv369dqxY4cOHz6sl19+WVWrVi2y/tSpU8rJyVHLli0lSeHh4a6eEuna\nb6P4+/tr06ZNSkxM1LZt2/TEE09o1qxZCgwMvM4rhW0tWkj79rm7Cni6DRvOhw5v0ry5lJTk7irg\njTwmbEhSSEiIpk2bpiVLligrK6vIugtjNpKSkjR8+HD16tXrusdQVK1aVSdPnnQtnzx5UtWqVXMt\nly9fXsHBwQoODlbNmjWVkJBA2CgDeDH1XImJUnCwlJ8vOZ3Sli0Sv1LArc8jbqNc6MXo3bu3nn32\nWTVu3Piy27Zo0UIhISF69913izxfuXJlVapUSXv27JF0foDn1QQEBOivf/2rzp07J+n8u14CAgIk\nSf/4xz/0008/SZIKCwt14MAB1atX79ovDoBrbpTAwPMBY8oUggbgTTyiZ+PiWxePP/74VbcfNGiQ\n+vbtqwEDBhR5ftKkSRo7dqzKlSun+++//6oDOjt16qSkpCT17NlTTqdTd9xxhyZMmCBJ+uc//6mx\nY8e6gkjLli312GOPlebyAFwkMJCQAXgbh/n14Igy7MyZM65bK/Pnz9eJEyc0evRoN1cF4MKMr3zW\nBuCdPKJn40b57LPPNH/+fBUUFKhevXqaPHmyu0sCAMDr3VI9GwA8Ez0bgHfziAGiAADg1kXPBgAA\nsIqeDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQNAABgFWEDgHUX5kYB4J0IGwAAwCrCBgAAsIqwAQAA\nrCJsAAAAqwgbAADAKuZGAQAAVtGzAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAsI65UQDv\nRtgAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFYxNwoAALCKng0AAGAVYQMAAFhF2AAAAFYRNgAA\ngFWEDQAAYBVhA4B1zI0CeDfCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirlRAACAVfRsAAAA\nqwgbAADAKsIGAACwirABAACsImwAAACrCBsArGNuFMC7ETYAAIBVZSZstGrVqtjnY2Ji9NFHH11x\n3/j4eB0/fty1/Morr+jQoUM3tD4AAFC8MhM2HA5HqfddvXq1MjIyXMuxsbH6z//8zxtRFgAAuAqP\nDBtDhw58Ed16AAAK+ElEQVRVr169FBERoRUrVkiSjDGaPHmywsPD9eSTTyorK+uS/ebMmaM+ffoo\nIiJC48aNkyRt2rRJSUlJGjlypKKjo3X27Fn169dP+/btkyStW7dOERERioiI0IwZM1zHatWqlWbO\nnKmoqCg98sgjyszMvAlXDsBbJSZKU6ee/w7ccowHOnnypDHGmF9++cWEh4ebrKws07RpU7Nu3Tpj\njDFxcXEmNjbWGGPMqFGjzKZNm4rsZ4wxI0eONJ9++qkxxpjHH3/c7Nu3z7Xu8ccfN0lJSSYjI8N0\n6tTJZGVlmYKCAtO/f3+TkJBgjDGmadOm5rPPPjPGGDNt2jQzd+5cuxcNWBYaaozkrq8G//pyZw18\nedJXaKi7fyNwMzndHXaKs3jxYiUkJEiSfvzxR/3www8qV66cunfvLkmKjIzUsGHDLtlv27ZtWrBg\ngXJzc/Xzzz+rcePG6tSpkyTJGHPJ9nv37lVAQICqVq0qSYqIiNCuXbvUpUsXlS9fXh07dpQkNW/e\nXNu2bbNxqbgBWrSQ/tVRBY+V4u4C4GE2bJCu4+44boDmzaWkpJtzLo8LGzt27FBiYqJWrFghX19f\n9evXT2fPnr1ku1+P4cjLy9Nrr72m1atXy9/fX3FxccXu92vFhRBJcjr/3TTlypVTfn7+NV4Jbpab\n9csC2JKYKAUHS/n5ktMpbdkiBQa6uyrgxvG4MRunTp1SlSpV5Ovrq0OHDumbb76RJBUUFGjjxo2S\npA8//FCtW7cust/Zs2flcDhUrVo15eTkaNOmTa51lSpV0unTpy85V8uWLbVz505lZ2eroKBA69ev\nV7t27SxeHQBcKjDwfMCYMoWggVuTx/VsBAcHa/ny5QoLC1OjRo1cb3mtWLGi9u7dq7lz56pGjRqa\nOXNmkf0qV66s3r17KywsTLVq1dK9997rWtezZ0+9+uqr8vPz0/Lly129IrVq1dJLL72kfv36SZI6\ndeqkzp07S7q+d78AwLUKDCRk4NblMJe7jwAAAHADeNxtFAAAcGshbACwjrlRAO9G2AAAAFYRNgAA\ngFWEDQAAYBVhAwAAWEXYAAAAVvE5GwAAwCp6NgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQN\nANYxNwrg3QgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAq5kYBAABW0bMBAACsImwAAACrCBsA\nAMAqwgYAALCKsAEAAKwibACwjrlRAO9G2AAAAFYRNgAAgFWEDQAAYBVhAwAAWEXYAAAAVjE3CgAA\nsIqeDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQNAABgFWEDgHXMjQJ4N8IGAACwirABAACsImwAAACr\nCBsAAMAqwgYAALCKuVEAAIBV9GwAAACrCBsAAMAqwgYAALDKK8PGH/7wh2vafseOHRoyZIilagAA\nuLV5Zdj4y1/+4u4SAADwGl4ZNlq1aiXp0h6L2NhYffDBB5KkL774Qt27d1fPnj310UcfuaVOwKbE\nRGnq1PPfbWNuFMC7Od1dgDs4HI4rrs/Ly9O4ceO0ZMkS3XHHHRo+fPhNqgxXExYmbdjg7ipQWlf5\n1bulhIZK69e7uwrAM3hl2Lia77//XnfccYfuuOMOSVJkZKTef/99N1d1/Vq0kPbtc3cVgHfYsMG7\nwhWk5s2lpCR3V+GZvDpslCtXThd/ptnZs2ddj2/FzzrjlwAXJCZKwcFSfr7kdEpbtkiBgfbOd+EO\nSkqKvXMA8FxeOWbjQpCoV6+eDh48qHPnzunnn3/Wtm3bJEl33XWX0tPTlZqaKklaT18objGBgecD\nxpQp9oMGAHhlz8aFMRt16tRR9+7dFR4ervr166t58+aSJF9fX02YMEGDBw+Wn5+f2rZtq5ycHHeW\nDNxwgYGEDAA3B3OjAAAAq7zyNgoAALh5CBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwCsY24UwLsR\nNgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVcyNAgAArKJnAwAAWEXYAAAAVhE2AACAVYQNAABg\nFWEDAABYRdgAYB1zowDejbABAACsImwAAACrCBsAAMAqwgYAALDK6e4CbnX5+fn68ccf3V0G4BGO\nHj3q7hIAWFSnTh05nZdGC+ZGsezo0aPq0qWLu8sAAMC6zZs3q379+pc8T9iwzNN7Nrp06aLNmze7\nu4wyhTYrHdqtdGi30qHdSud62+1yPRvcRrHM6XQWm/I8iafX54los9Kh3UqHdisd2q10bLQbA0QB\nAIBVhA0AAGAVYQMAAFhVbvz48ePdXQTcKyAgwN0llDm0WenQbqVDu5UO7VY6NtqNd6MAAACruI0C\nAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIG15o48aNCg8P13/9139p3759Rda9/fbb\n6tq1q7p3766tW7e6qULPFxcXpw4dOig6OlrR0dH64osv3F2SR/viiy/08MMPq1u3bpo/f767yykz\nQkJCFBkZqR49eqh3797uLsdjjR49Wu3bt1dERITruZMnT2rgwIHq1q2bnnrqKZ06dcqNFXqm4trN\n2mubgdc5dOiQOXz4sOnXr59JSkpyPX/w4EETFRVlzp07Z1JTU82DDz5oCgsL3Vip55o9e7ZZuHCh\nu8soEwoKCsyDDz5ojh49avLy8kxkZKQ5ePCgu8sqE0JCQkx2dra7y/B4O3fuNP/4xz9MeHi467lp\n06aZ+fPnG2OMefvtt8306dPdVZ7HKq7dbL220bPhhe666y41bNhQ5lef57Z582aFhoa6Zqpt0KCB\n9uzZ46YqPd+v2w/F27Nnjxo0aKB69eqpfPnyCgsLY+rvEjLGqLCw0N1leLy2bduqSpUqRZ7bvHmz\noqOjJUnR0dFKSEhwR2kerbh2k+y8thE24JKRkaG6deu6lv39/ZWRkeHGijzb0qVLFRUVpTFjxtBF\newXF/Vz99NNPbqyo7HA4HBo4cKB69eql999/393llCmZmZmqWbOmJKlWrVrKzMx0c0Vlh43XNucN\nOQo8zpNPPqkTJ05c8vyIESMUEhLihorKniu14aOPPqqhQ4fK4XBo5syZmjx5sl5//XU3VIlb2V/+\n8hfVrl1bmZmZevLJJ3XXXXepbdu27i6rTHI4HO4uoUyw9dpG2LhFLVq06Jr38ff317Fjx1zLP/74\no/z9/W9kWWVKSduwb9++GjJkiOVqyi5/f3+lp6e7ljMyMlS7dm03VlR2XGin6tWr66GHHtLevXsJ\nGyVUo0YNnThxQjVr1tTx48dVvXp1d5dUJlzcTjfytY3bKF7u4ntzISEh2rBhg/Ly8pSamqojR46o\nZcuWbqzOcx0/ftz1+OOPP1aTJk3cWI1nu/fee3XkyBGlpaUpLy9P69evV5cuXdxdlsfLzc1VTk6O\nJOnMmTPaunWrGjdu7OaqPNevxxmEhIRo9erVkqT4+Hh+5i7j1+1m67WNWV+9UEJCgmJjY5WVlaUq\nVaqoWbNm+t///V9J59/6unLlSjmdTo0ZM0ZBQUFurtYzvfzyy0pOTpaPj4/q1aun1157zXV/GJf6\n4osvNGnSJBlj1Lt3bw0ePNjdJXm81NRUPfvss3I4HCooKFBERATtdhkvvviitm/fruzsbNWsWVPP\nPfecHnzwQT3//PM6duyY6tWrp7feeqvYwZDerLh22759u5XXNsIGAACwitsoAADAKsIGAACwirAB\nAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKz6f2eBeyO8vtd4AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Predicted probabilities for 6 months after followup, 40 years of age."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_6_40'], ylabels=plot_labels)",
      "execution_count": 88,
      "outputs": [
        {
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc3fedcda58>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc3fedcdb70>",
            "image/png": 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S+b9fX9+1a5eCgoJUoUIFeXl5KSwsTN99950kqUyZMgoJCZEk1atXTy1atJCXl5fq16+v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\nAMAqwgYAALCKsAEAAKwibAAAAKv+P1zKu+cA3Bj7AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 12-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_12_40'], ylabels=plot_labels)",
      "execution_count": 89,
      "outputs": [
        {
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc3fedc2160>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc3fedc2b38>",
            "image/png": 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54CTzf7++vmvXLgUHB6tSpUry8vJSeHi4vv32W0lSuXLlFBoaKklq0KCBWrZsKS8vLzVs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\nP26EtEXp96eHAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_24_40'], ylabels=plot_labels)",
      "execution_count": 90,
      "outputs": [
        {
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc3fede6630>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc3fee75fd0>",
            "image/png": 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PPvus7rrrrku2bdSokdq1a6cPP/ywyPvlypVTYGCgduzYIalwgueVhISE6F//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\nsOr/AxMztpv+oFJcAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_6_30'], ylabels=plot_labels)",
      "execution_count": 91,
      "outputs": [
        {
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc42170b6a0>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc42170b358>",
            "image/png": 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dO3cqKChI5cuXl5eXl8LCwvTdd99JkkqVKqWQkBBJUt26ddWsWTN5eXmpXr16SkpKctWx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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_6_50'], ylabels=plot_labels)",
      "execution_count": 92,
      "outputs": [
        {
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc427541898>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc427541358>",
            "image/png": 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K51wlImyc68Xo1q2bbrnlFt19993avHnzRbetX7++WrdurdmzZ7ueVpGksmXL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\nAACwirABAACsImwAAACrCBsAAMCq/wPXYsKjddxemQAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_24_30'], ylabels=plot_labels)",
      "execution_count": 93,
      "outputs": [
        {
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc4277fb080>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc42781a748>",
            "image/png": 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k/u/b17dv366QkBCVKVNGPj4+ioiI0HfffSdJKlGihMLCwiRJtWrVUpMmTeTj46PatWsr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\nO8csMIHQuyEAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_myomectomy, varnames=['p_24_50'], ylabels=plot_labels)",
      "execution_count": 94,
      "outputs": [
        {
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc43e334048>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4277fd550>",
            "image/png": 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33XXJbZs0aaIOHTroww8/LPJ+hQoV5O/vr507d0oqHOB5Ja1atdK//vUvnT17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\nH1/G0V1YYV6JAAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "myomectomy_table = generate_table(myomectomy_model, trace_myomectomy)",
      "execution_count": 95,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "myomectomy_table_flat = myomectomy_table.reset_index().rename(columns = {'mean':'probability'}).drop(['sd'],\n                                                                                      axis=1)",
      "execution_count": 96,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "factorplot(myomectomy_table)",
      "execution_count": 97,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4070e4e10>",
            "image/png": 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NGkRwcDAjR47k0UcfJTo6GoPBQP/+/YHCOY8WLVpU4vkNBoP5de3atZk0aRLLli1jyZIl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\nbizLo90X5ECzkwMOJ590eOh/2d4birCroYNdDR2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I0yh2FuSsI0VfxtmukRfkOtdtCwq4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\nRK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Medical management model"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "med_manage_model = specify_model(pm.Model(), 'med_manage')",
      "execution_count": 83,
      "outputs": [
        {
          "name": "stdout",
          "text": "Applied log-transform to nu and added transformed nu_log_ to model.\nApplied log-transform to σ and added transformed σ_log_ to model.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "with med_manage_model:\n    \n    trace_med_manage = pm.sample(n_iterations, step=pm.Metropolis(), random_seed=20140925)\n",
      "execution_count": 84,
      "outputs": [
        {
          "name": "stdout",
          "text": " [-----------------100%-----------------] 100000 of 100000 complete in 655.5 sec",
          "output_type": "stream"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Baseline log-probabilities"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['μ'], ylabels=plot_labels)",
      "execution_count": 98,
      "outputs": [
        {
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc43d0dbf28>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc43d0dbf98>",
            "image/png": 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cjlWrpH/9P1WJ4OMj7d3r7CruP0eOHCnS9u6LyyhXRjG6du2qt956S48//niB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\npQgbAADAUoQNAABgKcIGAACw1P8DcGhSv+FSIqYAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_6_40'])",
      "execution_count": 99,
      "outputs": [
        {
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc414499390>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc4258f3cf8>",
            "image/png": 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XXXdVOnbixImaPn26XnnlFTVs2FCPP/74Ly4oICBAs2bN0vjx42VZloYNG3bBBaOStHfv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\nAAAA4wgcAADAOAIHAAAwjsABAACM+3/DFoq0S1RFtwAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 12-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_12_40'], ylabels=plot_labels)",
      "execution_count": 100,
      "outputs": [
        {
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc414472cf8>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc414472e80>",
            "image/png": 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M//36+s6dOxUcHKxKlSrJy8tL4eHh+vbbbyVJ5cqVU2hoqCSpQYMGatmypby8vNSwYUOl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\n1f8H8he1fUWw/hoAAAAASUVORK5CYII=\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 40."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_24_40'], ylabels=plot_labels)",
      "execution_count": 101,
      "outputs": [
        {
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc43319dfd0>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc43319d9b0>",
            "image/png": 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l2zZq1Ejt2rXThx9+WOT9cuXKKTAwUDt27JBUOMHzSlq0aKF//etfOnv2rKTC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\nNwAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_6_30'], ylabels=plot_labels)",
      "execution_count": 102,
      "outputs": [
        {
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc433ec8470>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc433ec8550>",
            "image/png": 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hkCRVqFDhXHCS+b9vX9+5c6datmypihUrysvLS6Ghofruu+8kSWXKlFFwcLAkqX79+mrR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\nF4lJKAAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 6-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_6_50'], ylabels=plot_labels)",
      "execution_count": 103,
      "outputs": [
        {
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc425e2f550>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc425e2fac8>",
            "image/png": 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nejF69OihZ599Vnfdddclt23UqJHatm2rDz/8sNDn5cqVk7+/v7Zt2yapYIDn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\n/w+UMLpAQSXuZgAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 30."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_24_30'], ylabels=plot_labels)",
      "execution_count": 104,
      "outputs": [
        {
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc423d47518>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc423d47fd0>",
            "image/png": 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m/359fefOnQoODlalSpXk5eWl8PBwffvtt5KkcuXKKTQ0VJLUoEEDtWzZUl5eXmrYsKFS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\n/weeGr1Xx0HBvQAAAABJRU5ErkJggg==\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Estimated probabilities of follow-up interventions for 24-month followup and age 50."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "pm.forestplot(trace_med_manage, varnames=['p_24_50'], ylabels=plot_labels)",
      "execution_count": 105,
      "outputs": [
        {
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.gridspec.GridSpec at 0x7fc432b369e8>"
          }
        },
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc422773ba8>",
            "image/png": 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9PvzwwyKfV6hQQf7+/tqxY4ekwgGeVxIcHKx//etfOnv2rKTCp16Cg4MlST/9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\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "med_manage_table = generate_table(med_manage_model, trace_med_manage)",
      "execution_count": 106,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "med_manage_table_flat = med_manage_table.reset_index().rename(columns = {'mean':'probability'}).drop(['sd'],\n                                                                                      axis=1)",
      "execution_count": 107,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "factorplot(med_manage_table)",
      "execution_count": 108,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc422af9390>",
            "image/png": 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SExMxmUx06dIFOP/Mo5kzZ172/CaTyb5cqVIlRo8ezdy5c5k9ezaDBw8mLCyMMWPGUKFC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\nAGISDS8AAAAAICbR8AIAAAAAYtL/A2o+4I/1eiO3AAAAAElFTkSuQmCC\n"
          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Summary tables"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "med_manage_table_flat['initial_treatment'] = 'medical management'\nuae_table_flat['initial_treatment'] = 'UAE'\nmyomectomy_table_flat['initial_treatment'] = 'myomectomy'",
      "execution_count": 109,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "all_tables = [med_manage_table_flat, uae_table_flat, myomectomy_table_flat]",
      "execution_count": 110,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "full_table = pd.concat(all_tables)",
      "execution_count": 111,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "full_table.head()",
      "execution_count": 112,
      "outputs": [
        {
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "   age  followup next intervention  probability  hpd_2.5  hpd_97.5  \\\n0   30         6           MRIgFUS        0.000    0.000     0.000   \n1   30         6          ablation        0.000    0.000     0.000   \n2   30         6      hysterectomy        0.016    0.008     0.026   \n3   30         6               iud        0.000    0.000     0.000   \n4   30         6        myomectomy        0.008    0.002     0.014   \n\n    initial_treatment  \n0  medical management  \n1  medical management  \n2  medical management  \n3  medical management  \n4  medical management  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>followup</th>\n      <th>next intervention</th>\n      <th>probability</th>\n      <th>hpd_2.5</th>\n      <th>hpd_97.5</th>\n      <th>initial_treatment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30</td>\n      <td>6</td>\n      <td>MRIgFUS</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>30</td>\n      <td>6</td>\n      <td>ablation</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>30</td>\n      <td>6</td>\n      <td>hysterectomy</td>\n      <td>0.016</td>\n      <td>0.008</td>\n      <td>0.026</td>\n      <td>medical management</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>30</td>\n      <td>6</td>\n      <td>iud</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>30</td>\n      <td>6</td>\n      <td>myomectomy</td>\n      <td>0.008</td>\n      <td>0.002</td>\n      <td>0.014</td>\n      <td>medical management</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "full_table['probability (95% CI)'] = full_table.apply(lambda x: '{0:1.2f} ({1:1.2f}, {2:1.2f})'.format(x['probability'], \n                                                                                        x['hpd_2.5'], \n                                                                                        x['hpd_97.5']), axis=1)",
      "execution_count": 113,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "full_table.head()",
      "execution_count": 114,
      "outputs": [
        {
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "   age  followup next intervention  probability  hpd_2.5  hpd_97.5  \\\n0   30         6           MRIgFUS        0.000    0.000     0.000   \n1   30         6          ablation        0.000    0.000     0.000   \n2   30         6      hysterectomy        0.016    0.008     0.026   \n3   30         6               iud        0.000    0.000     0.000   \n4   30         6        myomectomy        0.008    0.002     0.014   \n\n    initial_treatment probability (95% CI)  \n0  medical management    0.00 (0.00, 0.00)  \n1  medical management    0.00 (0.00, 0.00)  \n2  medical management    0.02 (0.01, 0.03)  \n3  medical management    0.00 (0.00, 0.00)  \n4  medical management    0.01 (0.00, 0.01)  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>followup</th>\n      <th>next intervention</th>\n      <th>probability</th>\n      <th>hpd_2.5</th>\n      <th>hpd_97.5</th>\n      <th>initial_treatment</th>\n      <th>probability (95% CI)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30</td>\n      <td>6</td>\n      <td>MRIgFUS</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>30</td>\n      <td>6</td>\n      <td>ablation</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>30</td>\n      <td>6</td>\n      <td>hysterectomy</td>\n      <td>0.016</td>\n      <td>0.008</td>\n      <td>0.026</td>\n      <td>medical management</td>\n      <td>0.02 (0.01, 0.03)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>30</td>\n      <td>6</td>\n      <td>iud</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>medical management</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>30</td>\n      <td>6</td>\n      <td>myomectomy</td>\n      <td>0.008</td>\n      <td>0.002</td>\n      <td>0.014</td>\n      <td>medical management</td>\n      <td>0.01 (0.00, 0.01)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "formatted_tables = {}\nfor inter,table in full_table.groupby('initial_treatment'):\n    table_pivot = table.assign(age_followup=table.age.astype(str)+'-'+table.followup.astype(str)).pivot(index='age_followup', \n                                                            columns='next intervention', \n                                                            values='probability (95% CI)')\n    age, followup = np.transpose([(int(a), int(f)) for a,f in table_pivot.index.str.split('-').values])\n    table_pivot = table_pivot.set_index([age, followup])\n    table_pivot.index.names = 'age', 'followup'\n    formatted_tables[inter] = (table_pivot[['none', 'uae', 'iud', 'myomectomy', 'hysterectomy', 'MRIgFUS']]\n                               .sortlevel([0,1]))",
      "execution_count": 115,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "formatted_tables['UAE']",
      "execution_count": 116,
      "outputs": [
        {
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "next intervention               none                uae                iud  \\\nage followup                                                                 \n30  6              0.59 (0.49, 0.70)  0.01 (0.00, 0.03)  0.00 (0.00, 0.00)   \n    12             0.60 (0.50, 0.70)  0.01 (0.00, 0.03)  0.00 (0.00, 0.00)   \n    24             0.62 (0.49, 0.74)  0.01 (0.00, 0.04)  0.00 (0.00, 0.00)   \n40  6              0.93 (0.91, 0.95)  0.01 (0.01, 0.02)  0.00 (0.00, 0.00)   \n    12             0.93 (0.91, 0.95)  0.02 (0.01, 0.03)  0.00 (0.00, 0.00)   \n    24             0.92 (0.89, 0.94)  0.02 (0.01, 0.03)  0.00 (0.00, 0.00)   \n50  6              0.54 (0.10, 0.87)  0.01 (0.00, 0.02)  0.34 (0.00, 0.86)   \n    12             0.59 (0.22, 0.86)  0.01 (0.00, 0.03)  0.27 (0.00, 0.69)   \n    24             0.63 (0.42, 0.83)  0.02 (0.00, 0.03)  0.15 (0.00, 0.40)   \n\nnext intervention         myomectomy       hysterectomy            MRIgFUS  \nage followup                                                                \n30  6              0.40 (0.29, 0.50)  0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  \n    12             0.39 (0.28, 0.48)  0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  \n    24             0.36 (0.23, 0.48)  0.01 (0.00, 0.01)  0.00 (0.00, 0.00)  \n40  6              0.04 (0.02, 0.05)  0.02 (0.01, 0.03)  0.00 (0.00, 0.00)  \n    12             0.03 (0.02, 0.05)  0.02 (0.01, 0.03)  0.00 (0.00, 0.00)  \n    24             0.03 (0.02, 0.04)  0.04 (0.02, 0.05)  0.00 (0.00, 0.00)  \n50  6              0.00 (0.00, 0.00)  0.07 (0.00, 0.14)  0.00 (0.00, 0.00)  \n    12             0.00 (0.00, 0.00)  0.09 (0.02, 0.18)  0.00 (0.00, 0.00)  \n    24             0.00 (0.00, 0.00)  0.15 (0.06, 0.28)  0.00 (0.00, 0.00)  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>next intervention</th>\n      <th>none</th>\n      <th>uae</th>\n      <th>iud</th>\n      <th>myomectomy</th>\n      <th>hysterectomy</th>\n      <th>MRIgFUS</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th>followup</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">30</th>\n      <th>6</th>\n      <td>0.59 (0.49, 0.70)</td>\n      <td>0.01 (0.00, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.40 (0.29, 0.50)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.60 (0.50, 0.70)</td>\n      <td>0.01 (0.00, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.39 (0.28, 0.48)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.62 (0.49, 0.74)</td>\n      <td>0.01 (0.00, 0.04)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.36 (0.23, 0.48)</td>\n      <td>0.01 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">40</th>\n      <th>6</th>\n      <td>0.93 (0.91, 0.95)</td>\n      <td>0.01 (0.01, 0.02)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.04 (0.02, 0.05)</td>\n      <td>0.02 (0.01, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.93 (0.91, 0.95)</td>\n      <td>0.02 (0.01, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.03 (0.02, 0.05)</td>\n      <td>0.02 (0.01, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.92 (0.89, 0.94)</td>\n      <td>0.02 (0.01, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.03 (0.02, 0.04)</td>\n      <td>0.04 (0.02, 0.05)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">50</th>\n      <th>6</th>\n      <td>0.54 (0.10, 0.87)</td>\n      <td>0.01 (0.00, 0.02)</td>\n      <td>0.34 (0.00, 0.86)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.07 (0.00, 0.14)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.59 (0.22, 0.86)</td>\n      <td>0.01 (0.00, 0.03)</td>\n      <td>0.27 (0.00, 0.69)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.09 (0.02, 0.18)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.63 (0.42, 0.83)</td>\n      <td>0.02 (0.00, 0.03)</td>\n      <td>0.15 (0.00, 0.40)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.15 (0.06, 0.28)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "formatted_tables['myomectomy']",
      "execution_count": 117,
      "outputs": [
        {
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "next intervention               none                uae                iud  \\\nage followup                                                                 \n30  6              0.58 (0.00, 0.97)  0.10 (0.00, 1.00)  0.15 (0.00, 1.00)   \n    12             0.84 (0.49, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    24             0.72 (0.25, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n40  6              0.99 (0.98, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    12             1.00 (0.99, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    24             1.00 (0.99, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n50  6              0.40 (0.00, 0.99)  0.13 (0.00, 1.00)  0.09 (0.00, 1.00)   \n    12             0.85 (0.00, 1.00)  0.05 (0.00, 0.33)  0.03 (0.00, 0.00)   \n    24             0.99 (0.95, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n\nnext intervention         myomectomy       hysterectomy            MRIgFUS  \nage followup                                                                \n30  6              0.09 (0.00, 0.37)  0.00 (0.00, 0.01)  0.05 (0.00, 0.37)  \n    12             0.16 (0.00, 0.50)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  \n    24             0.28 (0.00, 0.75)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  \n40  6              0.00 (0.00, 0.00)  0.01 (0.00, 0.02)  0.00 (0.00, 0.00)  \n    12             0.00 (0.00, 0.00)  0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  \n    24             0.00 (0.00, 0.01)  0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  \n50  6              0.00 (0.00, 0.00)  0.04 (0.00, 0.23)  0.11 (0.00, 1.00)  \n    12             0.00 (0.00, 0.00)  0.02 (0.00, 0.10)  0.03 (0.00, 0.00)  \n    24             0.00 (0.00, 0.00)  0.01 (0.00, 0.05)  0.00 (0.00, 0.00)  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>next intervention</th>\n      <th>none</th>\n      <th>uae</th>\n      <th>iud</th>\n      <th>myomectomy</th>\n      <th>hysterectomy</th>\n      <th>MRIgFUS</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th>followup</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">30</th>\n      <th>6</th>\n      <td>0.58 (0.00, 0.97)</td>\n      <td>0.10 (0.00, 1.00)</td>\n      <td>0.15 (0.00, 1.00)</td>\n      <td>0.09 (0.00, 0.37)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.05 (0.00, 0.37)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.84 (0.49, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.16 (0.00, 0.50)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.72 (0.25, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.28 (0.00, 0.75)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">40</th>\n      <th>6</th>\n      <td>0.99 (0.98, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.02)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1.00 (0.99, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1.00 (0.99, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">50</th>\n      <th>6</th>\n      <td>0.40 (0.00, 0.99)</td>\n      <td>0.13 (0.00, 1.00)</td>\n      <td>0.09 (0.00, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.04 (0.00, 0.23)</td>\n      <td>0.11 (0.00, 1.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.85 (0.00, 1.00)</td>\n      <td>0.05 (0.00, 0.33)</td>\n      <td>0.03 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.02 (0.00, 0.10)</td>\n      <td>0.03 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.99 (0.95, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.05)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "formatted_tables['medical management']",
      "execution_count": 118,
      "outputs": [
        {
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result",
          "data": {
            "text/plain": "next intervention               none                uae                iud  \\\nage followup                                                                 \n30  6              0.98 (0.96, 0.99)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    12             0.99 (0.98, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    24             0.99 (0.98, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n40  6              0.99 (0.98, 0.99)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    12             1.00 (0.99, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    24             0.99 (0.99, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n50  6              0.99 (0.99, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    12             1.00 (1.00, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n    24             0.99 (1.00, 1.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)   \n\nnext intervention         myomectomy       hysterectomy            MRIgFUS  \nage followup                                                                \n30  6              0.01 (0.00, 0.01)  0.02 (0.01, 0.03)  0.00 (0.00, 0.00)  \n    12             0.00 (0.00, 0.01)  0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  \n    24             0.00 (0.00, 0.01)  0.00 (0.00, 0.00)  0.01 (0.00, 0.00)  \n40  6              0.00 (0.00, 0.01)  0.01 (0.01, 0.01)  0.00 (0.00, 0.00)  \n    12             0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  \n    24             0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  \n50  6              0.00 (0.00, 0.00)  0.01 (0.00, 0.01)  0.00 (0.00, 0.00)  \n    12             0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  \n    24             0.00 (0.00, 0.00)  0.00 (0.00, 0.00)  0.01 (0.00, 0.00)  ",
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>next intervention</th>\n      <th>none</th>\n      <th>uae</th>\n      <th>iud</th>\n      <th>myomectomy</th>\n      <th>hysterectomy</th>\n      <th>MRIgFUS</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th>followup</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">30</th>\n      <th>6</th>\n      <td>0.98 (0.96, 0.99)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.01)</td>\n      <td>0.02 (0.01, 0.03)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.99 (0.98, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.99 (0.98, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">40</th>\n      <th>6</th>\n      <td>0.99 (0.98, 0.99)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.01)</td>\n      <td>0.01 (0.01, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1.00 (0.99, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.99 (0.99, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">50</th>\n      <th>6</th>\n      <td>0.99 (0.99, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.01)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1.00 (1.00, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.99 (1.00, 1.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.00 (0.00, 0.00)</td>\n      <td>0.01 (0.00, 0.00)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Model checking\n\nPosterior predictive checks for models. We generate simulated datasets using the model, and check where the observed data lies in the distribution of simulated values. Extreme values of the data percentile is suggestive of problems."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "ppc_uae = pm.sample_ppc(trace_uae, model=uae_model, samples=500)",
      "execution_count": 119,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "intervention_data = dataset[(dataset.intervention_cat=='uae')\n                            & ~dataset[outcome_cats].isnull().sum(axis=1).astype(bool)].copy()\n    \noutcomes = intervention_data[[ 'hysterectomy', 'myomectomy', 'uae',\n       'MRIgFUS', 'ablation', 'iud', 'no_treatment']].values",
      "execution_count": 120,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "from scipy.stats import percentileofscore",
      "execution_count": 121,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Calculate percentiles of each observation relative to simulated data."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "percentiles = []\n\nfor label in ppc_uae:\n    \n    if label.startswith('likelihood'):\n        \n        tokens = label.split('_')\n        index = int(tokens[1])\n        \n        simvals = ppc_uae[label]\n        obsvals = outcomes[index]\n        \n        p = [percentileofscore(s, o).round(2) for s,o in zip(simvals.T, obsvals)]\n        \n        percentiles.append(pd.Series(p, index=plot_labels))\n        \npd.concat(percentiles, axis=1).T.hist();",
      "execution_count": 124,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fc417211080>",
            "image/png": 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meKtXrwZjDFKpFAKBAGPHjlUqR0Jk0WkHTtM4Em3x4pWg5hnxoqOjZZ4RT91X\ngdR1pUaR6RJVdfVEm+KoIoaqrwINGTIEaWlpSschRF5UiY3ohA8++ACzZ89Gfn4+Jk+ejNTUVERH\nR9OMeISQbkuhQWyEdLW2rgQBNCMeIaT7ol/ghBBCiA7qtAOnCliEENK+4uJiREREwN/fH4GBgUhJ\nSdF0SqSb6LQDpwpYhBDSPj09PcTHx+PYsWP44YcfsHfvXpkLehCiDIVqoWdlZSE4OBhAUwWskydP\nqic7QgjRcpaWlnByaio8YmxsDHt7e5SWlmo4K9IdKHQPnCpgEUJIa0KhEHfv3sXw4cM1nQrpBlQy\niI0qYBFCuruamhrExMQgISEBxsbGmk6HdAMKPUamSAUsgGqhKxpTXt2lFjoh2kIsFiMmJgYCgQBT\np07tcNnuWAtdm8hT1VDeWujy0kgtdEUqYAFUC70tVAudEN2XkJAABwcHzJ8/v9Nlu2stdG2hzlro\n8u4TjdRCj46OxvLly5GamgpbW1skJSUpnAAhyvLy8oKJiQn4fD709fVx8OBBTadEupHc3FwcPXoU\ngwcPRlBQEHg8HlasWAFPT09Np0ZecgrVQgeoAhbRHjweD7t374aZmZmmUyHdkKurK/Ly8jSdBumG\nqBIb0XnNs0ARQkh3Qh040Xk8Hg9RUVEIDQ3F/v37NZ0OIYR0CZrMhOi877//HlZWVqisrERkZCQG\nDRoENzc3TadFCCFqpVQHToOHiDawsrICAPTu3Rs+Pj64detWux14VzzKqI7HDeV59EXeXHQtjrIx\nVP0oY0JCAs6cOQNzc3McPXpUqdwIkYdSHTgNHiKaVltbC6lUCmNjY4hEIly4cAFLly5td3l1P8qo\nrscNZX30RZFcdCmOKmKo+lHGkJAQhIeHIy4uTulYhMhDqQ6cBg8RTSsvL8fSpUvB4/EgkUgQGBiI\nCRMmaDot0o24ubmhsLBQ02mQbkjpX+BRUVHg8/mYNWsWZs6cqaq8CJFJ//79kZ6eruk0OBKJBPfu\n3et0ufz8/C7IhhDyMlOqA6fBQ4S0JBQKseyLTBiZWXW4XIUwD+b9nLooK0I6x6RSmb9YdpcvoBKJ\nROapYTWxT5TqwOUZPARQLXRFY8qLaqFrlpGZFUx62Xa4jKhKtW2d6A5trYVeW12Gj3eUw8is8w5L\nl7+AyjMgND09HV/svdnpF3JAsX3SJbXQ2yLv4CGAaqG3hWqhE6L7XpwvoiPaXAtdli+fgG5/AZWn\nFrqzszM6FeUTAAAgAElEQVSMzIrVtk/UXgu9PTR4iBBC2p4vIjQ0VNNpkW5A4Q5c2wYPEUKIJrQ3\nXwQh6kalVAkhhBAdRKVUCenEjZu3kfiPNLzS06jTZUsLbgG93LsgK0JId0cdOCGd+OOPalSiP3rq\n9ep02Ub+f9SfECGEQMlL6OfOncObb74JPz8/7NixQ1U5ESIzaoNEG1A7JJqg8C9wqVSKzz//HMnJ\nybCyssKMGTPg7e0Ne3t7VeZHSLuoDeo2eYpkSCQSAE11FkxNTTtd3t7eHnp6ekrlJytqh0RTFO7A\nb968CTs7O9jaNj0f5+/vj6ysLGq0pMtQG9RtDx48QHj8PpmLZBiamjct20mdBVFVKXZvmCP35C+K\nonZINEXhDrykpAR9+vTh/ra2tsatW7dUkhQhsqA2qPvkKRwi67Jdjdoh0RSND2JrvjRWXNx59TJ5\nas3eu3cPIpFI4bwUjff48WNUlz2EuO6PDperrS7H3btPcf78eVWlCED177umpoarnqYKzce5+bhr\ng87aYHl5GcS1T9HI67zalrihFjUyHH/R74WQNNR0uhzQ1FauXLkid6lhVbUFdcWR9bMCyL+/SkpK\nYGTU9lMDutgGmzHG0Fj3DDxRpUxx62trUF2p+n0sz7LaFFuez9K9e/dgaGioljZaU/kIgPJtUOEO\n3NraGkVFRdzfJSUlXG309nRUA3ju3LmKpqKVZKm0u+sesGvXLrXnoo18fX1bvSZvLXRtboOyHP8G\nGZcDgM8+O6pENtpN1n0gz/56993O95cq2iAgfzvUxHlQHftYnmW1KbYinyV15a1sG+QxeYr4Pkci\nkeDNN99EcnIyLC0tERYWhs2bN8t936eurg4jRozA8ePHVTroxNvbG1lZWVobT1diqjqeRCKBr68v\nbty4gZ49eyodS9vaoKr2lyriaFMuqoqjihiqbIPN8ZRth+o6D75IHecc2ob8VNUGFf4Frqenh48+\n+ghRUVFgjGHGjBkKDdpoTt7Ozk7RVNql6sky1DH5hi7EVEeOqjhxamsbVNX+UkUcbcpFVXFUlYsq\n2iCgmnaozvPgi7piEiHahmyUbYNK3QP39PSEp6enUgkQogxqg0QbUDskmkC10AkhhBAdRB04IYQQ\nooP0Pv300081nQQAeHh4aH1MXchRHTF1IUdVUFVO2hRHm3JRVRxtykXVuiIn2ob2bEPZ+AqPQieE\nEEKI5tAldEIIIUQHUQdOCCGE6CDqwAkhhBAdRB24GgUEBODy5ctyrxcfH48vv/xSDRkRQgh5WVAH\nrkYZGRkYPXq0ptMgRGmFhYVwdHSEVCrVdCqEkP+j9lHoUqkUISEhsLGxwddff427d+/i008/hUgk\ngq2tLf72t7/B2Ni41XrV1dVYs2YN7t+/Dz6fj8TERIwYMUKpmNu3b8eRI0fA5/MxePBgbNiwAQYG\nBvDy8oKJiQn4fD709fVx8OBBVFVVYcWKFSgsLES/fv2QlJQEU1PTVjHPnTuHxMREMMYQGhqK6Oho\nAFA4ZnFxMUJDQyGRSLi6yhEREUrl2LzPQkNDYW1tja+//lqpHDs6PsrEbO/4qFpDQwPmzp2LxsZG\nNDY2wtvbG7GxsXLtz2Yv7ldFYih7bJu1dUz+9Kc/yRwnPz8fK1asAI/HA2MMjx8/xvLlyyEQCLB4\n8WLk5uZi/Pjx+PLLLzvNpa1jWVtbK/d72rVrFw4ePAgA3GdBln2TkJCAM2fOwNzcHEePNk1e0dF6\n27dvR2pqKvT09LBmzRpMmDChw7xUrb3ziDKKi4sRFxeHiooK8Pl8ufZfZxwdHXHixAn0799fJZ+B\nF6WlpeHAgQPYt2+f0u1aFi+212vXriEhIQH79u1TeBtd0gaZmn333Xfsgw8+YO+99x5jjLHQ0FB2\n+fJlxhhjqampLCkpqc31Vq1axQ4ePMgYY6yxsZFVV1crFVMoFDIvLy9WX1/PGGNs+fLlLC0tjTHG\nmJeXF3v69GmL5Tdu3Mh27NjBGGNs+/bt7K9//WurmBKJhE2dOpUJhULW0NDApk+fzn777Tcu5qRJ\nk1h2djZbvXo1S0pK4mJevHiRubi4cDF//fVXFhwczFxcXNjixYvZggULWFJSEnv27Bnz9fVlv/32\nm8I5trfPlHnfjLV/fBSN2dHxUQeRSMQYY0wsFrOwsDB25coVufZnsxf364sxXFxc2DfffMMCAgLY\nqFGjWEJCAisvL2fvvvsuc3FxYZGRkWzy5MksMjKS7dmzh4u7ceNGNn78eHbixAm2fft2tnLlShYa\nGsrc3NzYjBkz2NWrV7ll582bx7Zs2cLGjBnDnJ2d2aJFi1hZWRlbvnw5Gzp0KPP09GSFhYXce/rt\nt99YZGQkc3d3Z2+++SbLzMzkYtXV1bENGzawyZMnsyFDhrAZM2awxMRE5urqyhwdHdnQoUPZ0KFD\n2fXr15lUKmVfffUVmzJlChs3bhxbtWoVq66uZkKhkHl6erIhQ4aw1NRUNnz4cDZy5EgWGRnJ1q1b\nxwIDA9nw4cNZUFAQY4yxhoYG5u7uzu7du8flUVFRwYYNG8beeustVl9fz8RiMYuMjGQFBQUyHafL\nly+zO3fusICAgBb7tK317t+/zwQCAWtsbGSPHz9mU6dOZVKptNNjryodnUeUUVpayu7cucMYYy3O\nJYq08xc5OjqyR48eMcba/gx8/fXXSsVPTU1lc+bMYYy1Ptf88ccfKnkPzdo693h4eLD3339fqW10\nRRtU6yX04uJinD17FmFhYdxr//nPf+Dm5gYAGDduHI4fP95qvWfPnuHKlSsIDQ0FAOjr68PExESp\nmCYmJujRowdqa2shFotRV1fHTfnHGGt1aTArKwvBwcEAgODgYJw8ebJVzJs3b8LOzg62trbo0aMH\n/P39udlrGGPg8XjtxjQyMsLJkyfR2NiIpUuXIigoCJcuXYJAIEBOTg4AwNjYGPb29igtLVU4x/b2\nmTLvu6Pjo2jMjo6POhgaGgJo+jUulUphZmYm8/5s1tZ+fTFGXV0dTpw4gV27duGnn37C6dOnsXDh\nQnzwwQf45ZdfIJFIUF1dDX9/f6Snp3NxMjMz0djYiClTpsDb2xsZGRmYP38+Ll68iHfeeQfvvfce\nqqqquOWPHTuGV155Bb/88gsePXqEuXPnYvbs2bC1tcXIkSOxbds2BAcH4/jx41iwYAGmT5+OnJwc\nbNmyBZ999hkePHgAAPjLX/6CO3fuYOXKlRg1ahTWrFmDM2fOIDk5GQBw6tQp9OvXDyNGjEBqairS\n09Oxe/dunDx5EjU1NVi3bh1MTEygr980zcL169fh4eGBP//5z8jOzsajR4+wa9cu/PDDD7h79y6u\nXLnCfXaOHDnCvZ+MjAy8/vrrcHFxgYGBAfT09ODm5objx4/j1KlTnR4nNzc3vPrqqy1ea+/4njp1\nCtOmTYO+vj769esHOzs73Lx5s8Njr0odnUeUYWlpCScnJwBAYGAg9PX18d577+Hbb7/F9evX0dDQ\ngODgYBw6dAi+vr7ccSotLe0w7rx588AYw/Tp0zFq1CikpqZi2LBhyM7Oxr/+9S989913uHPnDgDA\nysoKu3btwujRo/H222/j3//+Nxdnx44d8PHxgYuLCwICArjj8eDBA3z66ae4fv06Ro0ahfT0dISG\nhiI+Ph7r169HbGwsdu7ciRMnTqCsrAyPHj3Czp07MW3aNNy9e5eLX1paipiYGIwdOxZTp07F7t27\nuX/btm0b3n//faxatQqBgYEoKSnBtWvXIBaLcfXqVTx9+hQ//vgj/vGPf2Dnzp0ynQ9e1BVtUK0d\neGJiIuLi4lp0ZK+//jrXOH/88cc2J7AXCoXo1asX4uPjERwcjI8++gh1dXVKxTQzM0NUVBQmT54M\nT09PmJqaYty4cQAAHo+HqKgohIaG4sCBAwCAiooKWFhYAGj6IFRWVraKWVJSgj59+nB/W1tbc42f\nx+OhrKwMn376KfLz81vF1NPTQ2VlJa5fvw6xWIyIiAjo6enBz88Pzs7O3H64e/cuhg8frnCO7e0z\nZd53R8dH0ZgdHR91kEqlCAoKwvjx4+Hu7g4HBweZ92eztvbrizEkEgnmzZuH3r17w8rKCm5ubhgx\nYgQcHR1hYGAAHx8fNDY2IiUlBbdv3+Zub5SXl8Pf3x96enq4ffs2eDweAgMDwefz4e/vj0GDBuH0\n6dPcdidNmgRLS0usX78elZWVqK+vx4gRI1BZWYmgoCDk5eXB0tISpaWl6NevH4KCgsDj8eDo6Ahf\nX1/89NNPYIzh0KFDWLt2LX7++WcEBgZi5MiRqKysRK9evQAAFhYW3H7JyMjAO++8A1tbWxgaGiI2\nNhaZmZkwNTXFrFmzwBjD8ePHYWZmhoULFwIAQkND0atXLzg5OYHP53MneoFAgIyMDO79pKenY/r0\n6bhy5QqqqqpQW1uLc+fOobi4WO7j1KyysrLN9dr6HJeUlMgUUxU6Oo+oikQiQUFBAb799lsYGxsj\nPz8faWlpePDgASorK/H3v/8dFy5cQN++fREbG9thrD179gAAjhw5gokTJ2Ljxo0Amr4MV1dXw8jI\nCH/9619x584dbNy4ET169MClS5cwa9YsLF68GI2NjQCaZl77/vvvcfXqVSxZsgQffvghysvLYW9v\nj88++wwjR47E999/D2dnZ8THxyMrKwupqan485//DBMTExgaGmLWrFlwd3eHiYkJfH19kZiYCKDp\nh8SiRYvg5OSECxcuIDk5GSkpKfj555+593H69GkEBAQgNzcXY8eOxTvvvANPT094eHigb9++MDQ0\nxLVr17BgwQK52llHVN0G1daBnzlzBhYWFnBycgJ77jb7+vXrsW/fPoSGhkIkEqFHjx6t1hWLxbhz\n5w7mzJmDtLQ09OzZEzt27FAq5uPHj5GcnIzTp0/j/PnzEIlE3H2J77//HmlpafjXv/6FvXv34sqV\nK212dvL4/vvvYWVlhQ8++AAPHz7EkydP2oxZVlYGa2vrFq/b2tqisbERMTExSEhIgLGxscI5trfP\nlHnf7R0fZWJ2dHzUgc/n4/Dhwzh37hxyc3Nx8eJFuY55R/v1xRjm5ubc36+88kqrv4cNG4b09HQE\nBgZi9+7duHz5MhobGxEUFASg6ZcEn9/yo9q3b98WH3BTU1PumMyaNQvGxsbYsWMHeDweevbsCZFI\nBKDpxHb9+nW4u7vD3d0do0ePRkZGBioqKvD777+jvr4eNjY2OHXqFN58880290Pz36Wlpejbty/3\nuq2tLcRiMW7evIkDBw6Az+fj/PnzqK2t5X5dP//eeTwel9eIESPQs2dPXLp0CQ8fPsTjx4/x9ttv\nY+HChYiMjER0dDTX6be1jxWh6Hq6pqamBhUVFZg3bx4GDBgAPp+PKVOm4M6dOzh69CgMDAzg6OiI\nHj16IDY2FtevX0dRUVGncXNycrjPANC0P5ctWwYejwcDAwPs378fs2fPRo8ePcDj8RAUFAQDAwPc\nuHEDAODn58d1Zm+99VabvzqfP9d4e3vDzs4O586dA5/Ph4+PDwwNDTF9+nTw+fwWv8Bv3ryJp0+f\nYvHixdDT00O/fv0QFhaGY8eOcbFdXV0xceJECIVC5Ofnw8DAgGuvIpFI6T5AFsrGVGo60Y5cvXoV\np06dwtmzZ1FfX4+amhrExcVh48aN2LlzJ4CmS99nz55tta6NjQ1sbGwwbNgwAE0H+ptvvoFYLFY4\n5q1bt+Di4oLXXnsNAODj44Nr164hMDCQu1Tbu3dvTJ06FTdv3oS5uTnKy8thYWGBsrIy9O7du1VM\na2vrFg29pKSEi9X8/6amphgwYACEQiEXs6ysDBKJBL1794alpWWrb1qFhYW4ceMGwsPDMXXq1Bbx\n5M2xo+OgaMz2jo8yeXZ0fNTJxMQEnp6euH37tkx5Nmtrv3744YewsLBoEaOtDudFzQP1Zs+ejdOn\nT+Po0aPQ19fnOkdDQ8NWcYqKilpMX2lmZsYdk6ysLNja2uLOnTswNzfnLrWXlZXh1VdfxeDBg7nP\ny/MYY+jZsycOHz6MoUOHcu/f3Nwcv//+Oxej+XUrK6sW7b+wsBD6+voQCoVwdnaGUCiEnp4epk6d\nimvXroHP57fI5ZVXXmmx/eDgYKSnp8PCwgJ+fn4wMDBAaGgod6tmy5YtsLGxkes4Pa+99aytrfHk\nyRNuueLi4lZfqtWpo/OIssRiMWJiYmBkZIRJkyYBaNoPjDGIRCKUlZW1GJhlZGSE1157DSUlJS2+\nnLXl119/xZkzZ3D27FlUV1eDMYaEhATuM1BUVITDhw+jvr4e7u7uYIxBLBZzVxcOHz6M5ORkFBYW\nAgBqa2u5dtbsxXPN0KFDuXbd2NgIc3Nz7lg+/0W1qKgIJSUlcHd3B/DfW3vPPxXU/OXh1q1bGDZs\nGH766SfweDxMnToV2dnZePXVVxVqZx1RdRtU2y/w2NhYnDlzBllZWdi8eTM8PDywceNG7pKBVCrF\nP//5T8yePbvVuhYWFujTpw936TknJwf29vZKxRw0aBBu3LiB+vp6MMa4mLW1taipqQEAiEQiXLhw\nAYMHD4aXlxcOHToEoGlEpLe3d6uYw4YNw6NHj1BYWIiGhgYcO3YM3t7eLWLW19fj6dOnEAqFGD9+\nPHbv3o2UlBTU1tbC29sbI0eOhL6+Pnbv3g2xWIzjx4/j+vXr6N27N+bPnw8ASuXY3j5TJmZ7x0eZ\nmO0dH3WorKxEdXU1AKCurg7Z2dl44403ZMqzWVv79a9//SumTJnSIkbPnj07zKWxsRESiQQAMGTI\nENTV1eHnn3/G8OHDuTgVFRUAmu5zSyQSZGZm4uHDh5gyZQoXx8TEpMUxKS0thYODA7y8vHD+/Hku\nn2nTpiE/Px/p6ekQi8VobGzErVu38PDhQ/B4PISEhGD79u3w9PSEVCrF9evXMXnyZJw9exZ8Ph/J\nycncfvH390dycjKEQiFqamqwZcsW+Pv7w8HBAXl5eWCMccfSwcEBPXv2xLlz57hcbGxsWuyLwMBA\nnDx5EkePHoVAIOCOFdB0Qj5x4gQCAwNlPk4vXhlpbz0vLy9kZmaioaEBjx8/xqNHjzB8+PAOj5sq\ntXceUYWEhAQ4ODhwY1SApvd7584d8Hg8PHv2rEVHLRKJ8PTpU5k6jwULFnCfgSVLlsDAwKDFZ8DG\nxgaurq5YsGABLl26hMuXL+PatWuYNm0aioqK8NFHH+GTTz7B5cuXcfnyZTg4OHDHrPmX6YvnGqFQ\nyLXrq1evAmi7DfTp0wf9+vXDpUuXuG3n5uZyt6ieN2jQIOTl5QEA11579OiBUaNGyXw+aI+622CX\nPweekZEBPz8/TJs2DdbW1ggJCcHWrVtRWlqK9957j1tu7dq1WLlyJQQCAe7evYtFixZ1GnPs2LFc\nTAAtYjo6OkIgECAkJATTp08HYwwzZ85EeXk55syZg6CgIMyaNQs9e/bEhAkTsHDhQmRnZ8PPzw85\nOTncYx3Px9TT08NHH32EqKgoBAQEwN/fH/b29igvL4ePjw9KS0vx+eefIyQkhGsMKSkpePToEerr\n6xEdHY0ePXrgs88+Q1JSEjw8PLBv3z4wxiAUCrl7tJmZmS1y9PLykjnHZlu3bm3x94vvW5aYvr6+\nHR4fWWM2k+X4qENZWRkiIiIQFBSEmTNnwsvLC1euXGn3vcsjOjq6RYznB7Fs3bq11SWzZ8+e4ebN\nm9w+c3NzQ1FREdauXcvFuXHjBr766ivs3LkTY8aMwc6dO7F9+3aYmZkB+O/Jbu3atQgPD8f+/ftR\nVVWFRYsWYeHChbhz5w6EQiFycnKwZMkSfPvtt8jMzMTEiRMxceJEbNq0CQ0NDQCAmJgYVFdX45tv\nvoGHhwc2bdqEyMhIXL58GcbGxtwX0Js3b2LGjBkQCASYN28edzlz7dq1cHR0hI+PDxhjEAgE3LE0\nMTHB7du3uX0zePDgFvuiT58+cHJyQnV1NTcoddmyZQgICMCf//xnfPLJJzAxMZHpOH3wwQeYPXs2\n8vPzMXnyZKSmprY4Nvv37+fWc3BwwFtvvQV/f39ER0fjk08+6dLL683nkeDg4BbnEVm9+Nlulpub\ni6NHjyInJwelpaX45JNPcO7cOSxcuBCPHz9GZmYmpFIpN9amoaEBmzdvxogRIzr89b1161ZYWFjg\n8ePHbf57834+d+4cLl++jPHjx2Pr1q0QiUQ4e/YsRCIRamtrwefz0atXL0ilUqSmpuL+/ftcDHNz\ncxQXFyMpKYk712RlZaG8vJxr17/99htu3LjRog00d5jnzp2DsbEx/vWvf6G+vh4SiQT379/HrVu3\nWuXb3F6lUinGjx8Pxhjs7OwwcuRImc8HbR2DLmmDnQ1Tr6+vZzNmzGACgYBNmzaNbdq0iTHG2NOn\nT1lkZCTz9fVlUVFR7I8//uh0yHt7Bg8erPC6qo6jTbloWxxtyqVZcnIyCwgIYAEBAWzXrl1akZMy\n2zh8+DD3+Iy6tqEsdW4jISFB597Dw4cPmUAgYEFBQUwgEDAXFxeF2qKiOcmynpeXF8vOzub+3rp1\nK7feDz/8wKZOncrc3d3Ze++9x4qLizvd3g8//MDGjx/PRo8ezX788Ud28eJFNmnSpFbLnj9/noWG\nhrLBgwezCRMmsOXLl7OamhrGGGNbtmxh7u7ubMyYMewvf/kLmzdvHjtw4ABjrOnRwvfee48NHjyY\njRkzhjHGuMdxm+3fv5+Fh4dzfxcUFLChQ4dyOZaWlrLY2Fg2fvx45u7uzmbNmsXtg61bt7IPP/yQ\nW1coFDJHR0dun5w8eZJNnjyZjR49mn377bed7l9Fjp0q2qBMz4Gr6pnZ9mhTx6BNuWhbHG3KhTHG\n7t27xwICAlo8J9z8bKqmclJmGyKRiM2cOZOlp6erbRuqoK5tCIVCNnr0aJ1+DxKJhI0fP54VFRXJ\nva46O3BNr6cLOXb1eqpogzJdQlfFM7OEqNqDBw8wYsSIVs8J66ILFy5g3LhxsLS0REBAgKbT6XJf\nfvklAgMD8e6772o6FaVkZ2djwIABLR4JIkRdZBqF3ly69NGjR5g9e7ZCz8wSomqvv/46kpKSUFVV\nBQMDA5w7d457hl7XTJgwAdeuXdN0GhqzfPlyLF++HACwadMmDWejuMzMTPj7+2s6DaU1jwl5/j4s\na6M4FdEsmTrw5mdmnz17hgULFsj9zGxHmguAFBQUQE9PT6EYzxMKhVoR42WNo2yM5hHXdXV1nY7Q\n7oy9vT33nLCxsTGcnJwUakOqboMdUdWxfNm3oc74qmyDz2tsbMSpU6ewcuVKuddVtg0qur/aW8/G\nxqbdOgze3t4KbU/VOeryeqpqg3JPZvLVV1+hZ8+eOHjwIHbv3s09zxYREYEff/yxw3W3bt2Kbdu2\nKZwsebktXboUy5YtU3j95ueE33777XaXoTZIOqJMG8zKysK+ffvafMb+edQGSUfkaYOdduCVlZXo\n0aMHTE1NUVdXhwULFmDp0qW4cOECzMzMEB0djR07duCPP/5Q6JtnQUEBfH19sXfv3lbPhcrr9u3b\nSl9CVUWMlzWOKmIUFxdj7ty5OH78OOzs7JSKBTS1z969e6OoqAjvvvsu9u/f3+KZV1mosg22Jz8/\nHwn//BnGZrIVCKmpKkHi4vEYOHCgXNtRVXvR5DbUHV/VbbBZbGwsJk6cyI0NkocybVDR/dWV62lz\njvJ8Ntv7XMqbp6raYKeX0MvKyrB69Wquko1AIMDYsWPh5OSE999/H6mpqbC1tUVSUpJCCTRfLrKx\nsUG/fv0UitGspKSkwxgSiYSbtKE9IpEIIpEI9vb2Sl1O7SwXXYyjqlwAqOxS9bJly1BVVQV9fX3u\nOWFFc1FFG2yPSCSCfs9X0cNItmpO+vW1sLa2ljsfVR4jTW2jK94DoLo2CDQVW8rOzsa6deuUykWR\nNqjo/urK9bQ5R3k+m+19LhXNU9k22GkHPmTIEKSlpbV6/bXXXuNmKNIVDx48QHj8PhiZdVymULT3\nJnZvmNOq0ATRPnv37tV0CoTA0NCwRZEiQrqC2mqhaysjMyuY9LLVdBpERbZv344jR46Az+dj8ODB\n2LBhA1dbnBBCXmZdXkqVEFUpLCzE/v37kZaWhqNHj3J1wgkhpDvodr/AycvDxMQEPXr04Ooq19XV\nqWwWJ0II0Xb0C5zoLDMzM0RFRWHy5Mnw9PSEqakpxo0bp+m0SDdUXV2NmJgYbkKK5jmvCVEn6sCJ\nznr8+DGSk5Nx+vRpnD9/HiKRqN3iE4So0/r16zFp0iT8+OOPSE9PV9tUuIQ8r9NL6MXFxYiLi0NF\nRQX4fD5mzpyJ8PBwbNu2Dfv374e5uTkAYMWKFfD09FR7woQ0u3XrFlxcXPDaa68BAHx8fHDt2jUE\nBga2u05HRTRu376NkpISteRaUFAg9zq3b9/m5i2XR25urtzraNs21Bm/rKwMANqc31mRQi7Pnj3D\nlStX8Je//AUAoK+vr9DjjITIq9MOXE9PD/Hx8XByckJNTQ1CQkK4y5SRkZGIjIxUe5KEtGXQoEH4\n5z//ifr6ehgYGCAnJwfDhg3rcJ1ly5a1OkELhUJ4e3vD2dlZbc8fm5qaAhnFcq3j7Ows96OMubm5\ncHV1lWsdeal7G+qO31zyMisrSyXHWygUolevXoiPj8fdu3fh7OyMNWvWqLRMKyFt6bQDt7S0hKWl\nJQDA2NgY9vb2KC0tBfDfydMJ0QRHR0cIBAKEhISAz+fjjTfewMyZMzWdFulmxGIx7ty5g48//hjD\nhg3D+vXrsWPHDsTExGg6tZeGLEW4nl82Pz+/6UuzDPLz85VJTaPkGoUuFApx9+5dDB8+HLm5udiz\nZw/S09Ph7OyM1atXy7zDCFGVd999V+enoCS6zcbGBjY2NtzVHz8/P3zzzTftLq+O2ziK3nLoyvWU\n2VZBQQG+2Huz0yJcAFAhzIOhqTmMMktlil8hzIN5PyeZ82nv1pY8709Vt3Fk7sBramoQExODhIQE\nGHlAxSEAACAASURBVBsbY86cOViyZAl4PB62bNmCDRs2IDExUdZwhBDyUrCwsECfPn2Qn5+PgQMH\nIicnp8NBbKq+jaPoLYeuXE/ZbZmamsLIrFimIlyiqhK5CnaJquT7wtTWrS1535+qbuPI1IGLxWLE\nxMRAIBBg6tSpAIDevf9bN3bmzJlYtGhRp3G6YgBRR9+C5BlIpOgAIllz0dU4ysZQ9QCi/Px8rFix\nAjweD4wxPH78GMuXL0dERIRSeRIij7Vr12LlypUQi8Xo378/NmzYoOmUSDcgUweekJAABwcHzJ8/\nn3utrKyMuzd+4sQJmQbbqHsAUWffguQZSKTIACJ5ctHFOKqIoeoBRAMHDsThw4cBAFKpFJ6envDx\n8VE6LiHycHR0RGpqqqbTIN1Mpx14bm4ujh49isGDByMoKAg8Hg8rVqxARkYG8vLywOfzYWtrq/As\nPISoSnZ2NgYMGIA+ffpoOhVCCFG7TjtwV1dX5OXltXqdnvkm2iYzMxP+/v6aToMQQroE1UInL4XG\nxkacOnUKK1eu1HQqGiORSFBQUCDX0yDKzntPCNEc6sDJS+HcuXMYOnRoi8GVbXmZK7H991Eb2cZ5\niKpKsWrucNjZ2cmdG1Via8nLywsmJibg8/nQ19fHwYMHlc6TkM5QB05eCseOHUNAQECny73Mldjk\nedRG0W0AVImtLTweD7t374aZmZlK4hEiC5rMhOi82tpaZGdn0+hzojGMMUilUk2nQboZ+gVOdJ6h\noSFycnI0nQbpxng8HqKiosDn8zFr1iwq6Uu6hNyzkYWFhSEiIgJVVVVYsWIFCgsL0a9fPyQlJVEp\nVaIR1dXVWLNmDe7fvw8+n4/ExESMGDFC02mRbuT777+HlZUVKisrERkZiUGDBsHNzU3TaZGXnEKz\nkY0fPx6HDh3C2LFjsXDhQuzYsQPbt2/v1iOAieY0z8X897//HWKxGHV1dZpOiXQzVlZNNbp79+4N\nHx8f3Lp1q90OnGqhy7+eIoNA1UWnaqG3NRtZSUkJsrKysGfPHgBAcHAwwsPDqQMnXY7mYiaaVltb\nC6lUCmNjY4hEIly4cAFLly5td3mqhS7/eooMAlUXnauF/vxG7969ixEjRqCiogIWFhYAmjr5yspK\nhZMgRFE0FzPRtPLycixduhQ8Hg8SiQSBgYGYMGGCptMi3YDCs5HxeLwW//7i34R0BZqLmWha//79\nkZ6eruk0SDek8Gxk5ubmKC8vh4WFBcrKyjotoAHQbGQvQxxtm41MG+ZilkVXFXJR9zaaUSEXQjRP\n4dnIvLy8cOjQIURHRyMtLa3ND8OLaDYy3Y6jjbORaXouZll1VSEXdW8DoEIuhGgLhWcjW7hwId5/\n/32kpqbC1tYWSUlJXZEvIa3QXMyEkO5I4dnIACA5OVnV+RAiN5qLmRDSHVEpVUIIUZJUKkVwcDAW\nLVqk6VRIN0KlVInOo5mgiKalpKTA3t4ez54903QqpBuhDpzoPJoJimhScXExzp49i0WLFuG7777T\ndDqkG6EOnOg8mgmKaFJiYiLi4uKUfuyUaD8mlSI/P7/V6wUFBe3OBWJvbw89PT215EMdONF5NBMU\n0ZQzZ87AwsICTk5OuHjxokzryFKL4H+zLuCXO7/LnMfEYdnwnjxO5uWbUS10+dRWl+HjHeUwMnvQ\n+h/beIRTVFWKVXOHw87OrsXrXVYLnRBtRzNBdV8SiQQPHrRxMu2AKov1XL16FadOncLZs2dRX1+P\nmpoaxMXFYePGje2uI0stgss37gHmf5I5j94WdV1en7wrt6VNtdCNzKxg0stW5uXbqrXQZbXQExIS\ncObMGZibm+Po0aMAgG3btmH//v0wNzcHAKxYsQKenp4KJ0GIMjQ9E5QsqBKbeuIXFBTgi703YWRm\nJXPs6rKHAFRTiS02NhaxsbEAgEuXLuHbb7/tsPMmRJU67cBDQkIQHh6OuLi4Fq9HRkYiMjJSbYkR\nIgtNzwQlK6rEpp74pqamMDIrlusXkbjuD1SDKrER3ddpB+7m5obCwsJWrzPG1JIQIfKgmaCItnB3\nd4e7u7um0yDdiML3wPfs2YP09HQ4Oztj9erV7Y7AI0SdaCYoQkh3pVAHPmfOHCxZsgQ8Hg9btmzB\nhg0bkJiY2Ol6NBuZ7sfRttnIgKYqWKGhobC2tsbXX3+tVH6EEKIrFOrAn586dObMmTKXD6TZyHQ7\njjbORgZQFSxCSPckUy30F+93N/+KAoATJ04o1dERoozmKlhhYWGaToV0Uw0NDQgLC0NQUBD8/f2x\nefNmTadEuolOf4F/8MEHuHjxIp4+fYrJkydj2bJluHjxIvLy8sDn82Fra4t169Z1Ra6EtEJVsIim\nGRgYICUlBYaGhpBIJHj77bfVPlKfEECGDnzTpk2tXgsNDVVLMoTIQ5EqWLqivZKNHZF3eaI6hoaG\nAJp+jUulUqrLT7oEVWIjOkuRKli6Usilw5KN7agQ5sG8n5Nc23kZCrkoStUDKUNCQvDo0SPMnj0b\nDg4OCuelqzqqitderXB11gnvDqgDJzpLkSpYulTIRd6SjaIq+b98vAyFXBQtsanKgZR8Ph+HDx/G\ns2fPEBUVhUuXLrX7TLgsXyKbBnvKflweP37U5fXJX9RpVbwXjlN7dcLb2pa21EJXRFtfkqkWOiGE\naBkTExNMmjQJt2/fbrcDl7UW+o0q2bfbv/8AjddCV6QqXmdfILWxFrq81FkLXaZR6IRoO3d3d3oG\nnGhEZWUl9wurrq4O2dnZcHKS71YGIYqgX+CEEKKEsrIyrF69mpuXXiAQYOzYsZpOi3QD/7+9u4+K\n6jr/Bf6dASwIiCKvSw1ViEriS5tQreBFA4gkiAhI3kz1SlPNWlGsmpCKMU2balaSFeuNvauBxMSa\n601rUXwJ5hcbCKLLhQkmEV1qTCxFXsIoEhBhEGZm3z+8zE8NM5y3mWHg+/mP8ZxnP+ewOdtzZp9n\nK1qNrK2tDWvXrkVDQwPGjh2Lbdu2sZQquUR3dzeWLFmCnp4e9PT0IDEx0fq9OJEzTJo0CcXFxa5O\ng4agfh+hZ2ZmYseOHXd8VlhYiFmzZuGTTz7BzJkzUVBQ4LAEiezpfQd3//79OHjwICorKx0+Q5qI\naCDodwCPiYnBiBEj7vistLQUGRkZAICMjAx8+umnjsmOSAK+g0tEQ5GiSWwtLS0ICgoCAAQHB6Ol\npUXTpIjksFgsWLRoEeLi4jBjxowh+Q4uEQ09mkxi0+l0WoQhUkTOO7jkXPaKe9jbx500NTUhLy8P\n165dg16vR3Z2NpYuXerqtGgIUDSAjx49Gs3NzQgKCsLVq1fvWJ3MHi4n6v5xBuJyor2kvIPrLpXY\nnMXRldj6Le5xl97iHlKrcw2ESmweHh7YsGEDoqOj0dHRgczMTMTFxSEyMlJxbkRSSBrA716NLCEh\nAfv27cOKFStQXFzc5x9CX7icqHvHGYjLiba0tMDLywv+/v7Wd3BXrVplc3t3qsTmDI6uxKakuAcA\nt6rEFhwcjODgYACAr68vIiMjceXKFQ7g5HCKViNbsWIF1qxZg71792LMmDHYtm2bM3Il+hG+g0sD\nSX19PS5cuIBp06a5OhUaAhStRgYAO3fu1DoXItn4Di4NFB0dHcjNzUV+fj58fX1dnQ4NAazERm6N\nE4hoIDCZTMjNzUV6ejqSkpLsbjuYFzORS8ocDC5mYhsHcHJrnEBEA0F+fj6ioqKwbNmyfrcdzIuZ\nyJ2PwMVMuJgJDWHBwcHWhSNun0BE5CynTp3CoUOHUFlZiUWLFiEjIwMVFRWuTouGAN6B06DBCUTk\nCg8++CDOnz/v6jRoCOIAToMCJxA5Xm9RltraWsmLF9XU1Dg4K6KhS9UAnpCQAD8/P+j1enh6eqKo\nqEirvIgk03oCkSMM1Ek4cgq53FGUReL3kdfqz2P0WPlrY8spFKOUI4oJETmTqgFcp9Phgw8+4OIR\n5FJaTyByhIE6CUdOIRclRVk625T9h8idCrkQuYqqSWy9xTOIXIUTiMjV8vPzERsbi7S0NFenQkOM\n6jvwnJwc6PV6PPbYY3j00Ue1yotIEk4gIlfLzMzEr371K+Tl5bk6FRpiVA3gH374IUJCQtDS0oLl\ny5djwoQJiImJ0So3IqIBLyYmBg0NDa5Og4YgVQN4SMitFYYCAwMxb948nDlzxu4AztXI3D/OQFuN\nLD8/H+Xl5Rg9ejQOHTqkKjciIneieAA3Go2wWCzw9fVFZ2cnjh8/bncVKICrkbl7nIG4GhkfX9JQ\nJyxmNDU14uLFi5L3kVqpsK/13G29RshXBp1P8QDe3NyMVatWQafTwWw2Iy0tDbNnz9YyN6J+8fEl\nuRuta6F3tDXh0OU2lH7zqaTte9dcj4iI6PeJms313Pu4EVLyyiBrobuoFvq4ceNw4MABpbsTEQ0a\nQgjJ2zqiFvrwgBBZr/dNmTIF7e3t/T5Rk/PqoJJXBlkLXd2TSFZiI/r/tvyv/4Phfv3XNLBYLBjx\nk248uThVcmw+Xhy81q9fj5MnT6K1tRVz587F6tWrkZWV5eq0aAjgAE5Dir3Hl/++EQovS2C/MW78\n0IDOtjYc/U7aI0tAeUUyR5Nbic1Z3KkS25tvvqk4ByI1OICT29Pq8aUcch9ZKq1I5mhyK7E56zEm\nK7ER9Y/LiZJbW79+PR5//HHU1NRg7ty52Lt3r6tTIiJyCt6Bk1vj40siGqpU3YFXVFQgJSUF8+fP\nR2FhoVY5EUnGPkgDAfshuYLiO3CLxYJXXnkFO3fuREhICBYvXozExETJBQK0YrFYrAuqmEwmmEwm\nm9uazWbN2++r0AFgu9hBZGQkPDw8NM/DlWydg75ouVznQOmDNLSxH5KrKB7Aq6urERERgTFjbk3k\nSU1NRWlpqdM77Z//9/s4UnVrEktPTw+8vGzPDB6puwLoJ2na/qVLl/CrDf/3x4UOgB9Nrulsu4IP\nXn1SVYW3gcjuObhL+9V/a9buQOmDNLSxH5KrKB7ADQYDwsPDrT+HhobizJkzmiQlh85jGIYFTQEA\nDOtn2590nQKM2ucgd0byYCT1HJi6rkNdhfn/NlD6IA1t7IfkKi6fxNb7WLupSdmrIK0tzWhrvPUu\nqBACOp3O5rYeuqtob++Eqeu63ZjG9mZUVVVJetxbV1eH9qv/7jem3Li9Ll68iM7OTsnbOzKOrRhy\nzkFHy2UAjvk6Q6neXNoN38DjJ379bm9svwah85B0vL06f2iAubtD8j5yt1eyj9z+KOf3rCanCxda\ncezYMYflNJD7YO91sPWHFrQ1SvtaqvN6M6D3lP17NxqN/V4T5JxfR/S/3muOI/OQs72SYzQYDBg+\nfPgdn/f+ntX2QZ2Q8xLtbb7++mts374dO3bsAADrxI0VK1bY3MdeEQ0iuUU02AdJa0oKucjth+yD\nZI+cPqh4ADebzUhJScHOnTsRHByM7OxsbN26Vfb3Pl1dXZg+fTqOHDmienJXYmIiSktLXR5jsMbR\nIobZbEZycjJOnz4Nb29v1bEGWh+0R6vf5WBvw9HxteyDvfHU9kM1fVDp+XLmfu6QozP306oPKn6E\n7uHhgU2bNiEnJwdCCCxevFjRpI3e5CMiIpSmcgctKitpVZ1pMMbRKhctLpwDtQ/a44zKX4OhDWcc\ngxZ9ENCmH6rtg0rPlzP3c4ccnb2f2j6o6jvw+Ph4xMfHq0qASA32QRoI2A/JFVhKlYiIyA1xACci\nInJDHi+//PLLrk4CAGbOnDlg4gykXAZanIGUi9ackRPbGBjxndWGXEpzcof93CFHZ++ntg8qnoVO\nRERErsNH6ERERG6IAzgREZEb4gBORETkhjiAExERuSEO4ERERG7IqauRdXd3Y8mSJejp6UFPTw8S\nExOxbt06tLW1Ye3atWhoaMDYsWOxbds2+Pv79xvPYrEgKysLoaGhePvttxXFSUhIgJ+fH/R6PTw9\nPVFUVCQ7Tnt7OzZu3Ihvv/0Wer0eW7ZswU9/+lNZMWpqarB27VrodDoIIVBXV4c1a9YgPT1d9jEV\nFBTg4MGD0Ov1mDhxIl599VUYjUZZcf72t7+hqKgIAJCdnY2lS5dKPi/5+fkoLy/H6NGjcejQIQCw\nu29BQQH27t0LDw8PbNy4EbNnz7Z7fFqrqKjAli1bIIRAVlaW3cVQpGhqakJeXh6uXbsGvV4v+/zJ\nocXfgD1a9O3+aNFf7zZY+6Dc4wKU90e112slfVPJ9VhJH1VzvVXaX9VcU20STtbZ2SmEEMJkMons\n7GxRVVUlXn/9dVFYWCiEEKKgoEC88cYbkmK9//77Yv369WLlypVCCKEoTkJCgmhtbb3jM7lxXnjh\nBVFUVCSEEKKnp0dMmjRJvPjii9YYGRkZ4rHHHhNCCNHW1iZWrlwpfvnLX4oZM2aIlStXiqamJmus\n9vZ2sWHDBjF58mQRFxcnHn30UVFQUCA5l/r6epGQkCBu3rwphBBizZo1Yt++fbKO6eLFi2LBggXi\n5s2bwmQyieXLl4va2lrJMb744gtx7tw5sWDBAutntvb99ttvRXp6uujp6RF1dXUiKSlJWCwWu8eo\nJbPZLJKSkkR9fb3o7u4WCxcuFN99952qmFeuXBHnzp0TQghx48YNkZycLL777jvF/dweLf4G7Lm7\nb1+/fl3TNrTor30ZrH1QznH1UtMf1VyvlfRNJddjtX3UbDaLuLg40djY2O9+Svur2muqLU5/hO7j\n4wPg1v/uLBYLAgICUFpaioyMDABARkYGPv30037jNDU14ejRo8jOzrZ+piSOEAIWi+WOz+TEuXHj\nBqqqqpCVlQUA8PT0hE6nw4kTJ6wxIiIiUFNTA+C//1d69OhRfPbZZ/D29sYf//hHa7wXXngB165d\nw/Tp03Hw4EGcP38enp6eko/Jz88PXl5eMBqNMJlM6OrqQmhoqKxjunTpEqZPn45hw4bBw8MDMTEx\nOHLkCMrKyiTFiImJwYgRI+74zFb7ZWVleOSRR+Dp6YmxY8ciIiIC1dXVdo9RS9XV1YiIiMCYMWPg\n5eWF1NRU1SthBQcHIzo6GgDg6+uLyMhIGAwGRf3THq3+Bmzpq2/7+/tr2oYW/bUvg7UPyjmuXmr6\no9LrtdK+Kfd6rEUfPXHiBO655x6Eh4f3u5/S/qr2mmqL0wdwi8WCRYsWIS4uDjNmzEBUVBSuXbuG\noKAgALc6W0tLS79xtmzZgry8POh0OutnSuLodDrk5OQgKysL//znP2XHqa+vx6hRo7BhwwZkZGRg\n06ZNEEKgtbXVGsPb2xtGoxEAMHLkSMybNw/Dhg3D8OHDsXL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          },
          "output_type": "display_data",
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "mimetype": "text/x-python",
      "version": "3.5.1",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "nbconvert_exporter": "python",
      "file_extension": ".py",
      "pygments_lexer": "ipython3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "UF Interventions Meta-analysis",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}