Bleeding MA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "rseed = 20090425"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Citation</th>\n",
       "      <th>REFID</th>\n",
       "      <th>Child Paper</th>\n",
       "      <th>Number of \n",
       "Arms</th>\n",
       "      <th>Rx Grouping</th>\n",
       "      <th>Group_Desc</th>\n",
       "      <th>Intervention_category</th>\n",
       "      <th>Dose</th>\n",
       "      <th>Route</th>\n",
       "      <th>Rx_Durn</th>\n",
       "      <th>...</th>\n",
       "      <th>outcome_mean</th>\n",
       "      <th>outcome_sd</th>\n",
       "      <th>outcome_se</th>\n",
       "      <th>outcome_med</th>\n",
       "      <th>outcome_q1</th>\n",
       "      <th>outcome_q3</th>\n",
       "      <th>outcome_min</th>\n",
       "      <th>outcome_max</th>\n",
       "      <th>outcome_lo_95</th>\n",
       "      <th>outcome_hi_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>G1:</td>\n",
       "      <td>Celecoxib</td>\n",
       "      <td>Preoperative and postoperative NSAID</td>\n",
       "      <td>6 mg/kg and 3 mg/kg post-op</td>\n",
       "      <td>Oral</td>\n",
       "      <td>pre-op and post-op 5 days</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>G2:</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>6 mg/kg and 3 mg/kg post-op</td>\n",
       "      <td>Oral</td>\n",
       "      <td>pre-op and post-op 5 days</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>G1:</td>\n",
       "      <td>Celecoxib</td>\n",
       "      <td>Preoperative and postoperative NSAID</td>\n",
       "      <td>6 mg/kg and 3 mg/kg post-op</td>\n",
       "      <td>Oral</td>\n",
       "      <td>pre-op and post-op 5 days</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>G2:</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>Placebo</td>\n",
       "      <td>6 mg/kg and 3 mg/kg post-op</td>\n",
       "      <td>Oral</td>\n",
       "      <td>pre-op and post-op 5 days</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H. S. Abdel-Ghaffar, H. G. Abdel-Azeem and M. ...</td>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>G1a:</td>\n",
       "      <td>Lornoxicam in one tonsil</td>\n",
       "      <td>perioperative NSAID</td>\n",
       "      <td>8 mg</td>\n",
       "      <td>Instillation/infiltration</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Citation REFID  Child Paper  \\\n",
       "0  K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...   109          NaN   \n",
       "1  K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...   109          NaN   \n",
       "2  K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...   109          NaN   \n",
       "3  K. Murto, C. Lamontagne, C. McFaul, J. MacCorm...   109          NaN   \n",
       "4  H. S. Abdel-Ghaffar, H. G. Abdel-Azeem and M. ...   253          NaN   \n",
       "\n",
       "   Number of \\nArms Rx Grouping                 Group_Desc  \\\n",
       "0                 2         G1:                  Celecoxib   \n",
       "1                 2         G2:                    Placebo   \n",
       "2                 2         G1:                  Celecoxib   \n",
       "3                 2         G2:                    Placebo   \n",
       "4                 2        G1a:  Lornoxicam in one tonsil    \n",
       "\n",
       "                  Intervention_category                         Dose  \\\n",
       "0  Preoperative and postoperative NSAID  6 mg/kg and 3 mg/kg post-op   \n",
       "1                               Placebo  6 mg/kg and 3 mg/kg post-op   \n",
       "2  Preoperative and postoperative NSAID  6 mg/kg and 3 mg/kg post-op   \n",
       "3                               Placebo  6 mg/kg and 3 mg/kg post-op   \n",
       "4                   perioperative NSAID                         8 mg   \n",
       "\n",
       "                       Route                    Rx_Durn      ...        \\\n",
       "0                       Oral  pre-op and post-op 5 days      ...         \n",
       "1                       Oral  pre-op and post-op 5 days      ...         \n",
       "2                       Oral  pre-op and post-op 5 days      ...         \n",
       "3                       Oral  pre-op and post-op 5 days      ...         \n",
       "4  Instillation/infiltration                        NaN      ...         \n",
       "\n",
       "  outcome_mean outcome_sd outcome_se outcome_med outcome_q1 outcome_q3  \\\n",
       "0          NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "1          NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "2          NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "3          NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "4          NaN        NaN        NaN         NaN        NaN        NaN   \n",
       "\n",
       "   outcome_min  outcome_max  outcome_lo_95  outcome_hi_95  \n",
       "0          NaN          NaN           -7.0            8.0  \n",
       "1          NaN          NaN            NaN            NaN  \n",
       "2          NaN          NaN           -4.0            6.0  \n",
       "3          NaN          NaN            NaN            NaN  \n",
       "4          NaN          NaN            NaN            NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_file = 'data/Bleeding_Dataonly_For Chris_04152016.xlsx'\n",
    "\n",
    "raw_data = (pd.read_excel(data_file, sheetname='Bleeding_Outcomes_AllKQs',\n",
    "                         na_values=['null', 'ND'])\n",
    "            .drop(['Comments', 'Other stats \\nName','Other Stats','Results','Comments 2',\n",
    "                   'Presentation \\nlocation','Last Assesment tmpt for the study','Followup duration category'], \n",
    "                  axis=1)\n",
    "            .rename(columns={'Outcome timepoint (when was this outcome measured, e.g., in PACU, 12 months post─op, immediately post─op──would need a row for each outcome at each timepoint of interest)': 'outcome_time',\n",
    "                             'OUTC_Main_\\nCATG':'outcome_cat',\n",
    "                            \"Outc_SUB_\\nCATG\":'outcome_subcat',\n",
    "                            'OUTCOME SAMPLE SIZE': 'N',\n",
    "                             'Outcome\\ncount': 'outcome_obs',\n",
    "                            'Outcome  \\n%': 'outcome_pct',\n",
    "                            \"Outcome\\n Mean\": 'outcome_mean',\n",
    "                            \"Outcome \\nSD\": 'outcome_sd',\n",
    "                             'Outcome \\n SE': 'outcome_se',\n",
    "                             'Outcome\\n _Q1': 'outcome_q1', \n",
    "                             'Outcome \\n_Q3': 'outcome_q3',\n",
    "                            \"Outcome \\n Median\": 'outcome_med',\n",
    "                            \"Outcome \\n 95% L\": 'outcome_lo_95',\n",
    "                            \"Outcome \\n 95% H\": 'outcome_hi_95',\n",
    "                            \"Outcome\\n  Min\": 'outcome_min', \n",
    "                             'Outcome \\n Max': 'outcome_max'}))\n",
    "\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raw_data['Intervention_category'] = raw_data.Intervention_category.str.lower().str.strip()\n",
    "clean_data = raw_data.replace({'Intervention_category':{'saline':'control',\n",
    "                        'control (no rx)':'control',\n",
    "                        'control (no dexamethasone)':'control',\n",
    "                        'no antibiotics':'control',\n",
    "                        'no steroid':'control',\n",
    "                        'placebo':'control'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counts of bleeding outcomes by major category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reoperation-bleeding            162\n",
       "Revisit/Readmission-bleeding    150\n",
       "Undefined bleeding              121\n",
       "Secondary bleeding              113\n",
       "Primary bleeding                110\n",
       "Name: New_Bleeding_specify, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.New_Bleeding_specify.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the full list of intervention categories. For the purposes of this analysis, I will just use the ones with \"total\" or \"partial\" in the name, and take them to be surgical interventions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total cold                                                127\n",
       "total electrocautery                                      124\n",
       "total coblation                                            69\n",
       "control                                                    48\n",
       "perioperative steroid                                      47\n",
       "total harmonic scalpel                                     28\n",
       "partial coblation                                          25\n",
       "total molecular resonance                                  20\n",
       "perioperative nsaid                                        17\n",
       "partial microdebrider                                      15\n",
       "partial laser                                              15\n",
       "total laser                                                13\n",
       "partial cold                                               13\n",
       "total thermal welding                                      12\n",
       "postoperative analgesic                                     9\n",
       "total tonsillectomy - unspecified                           6\n",
       "perioperative anesthetic                                    5\n",
       "medical treatment                                           4\n",
       "total adenotonsillectomy - unspecified                      4\n",
       "total adenotonsillectomy - unspecified + no surgery         4\n",
       "total electrocautery + total cold                           4\n",
       "perioperative local anesthetic                              4\n",
       "postoperative nsaid                                         4\n",
       "salvia officinalis oral rinse                               3\n",
       "total microdebrider                                         3\n",
       "perioperative opiate analgesic                              3\n",
       "total argon plasma                                          3\n",
       "total plasmaknife                                           3\n",
       "postoperative steroid                                       3\n",
       "perioperative opioid analgesic                              3\n",
       "no surgery                                                  2\n",
       "preoperative analgesic                                      2\n",
       "perioperative analgesic                                     2\n",
       "preoperative and postoperative nsaid                        2\n",
       "postoperative antibiotic with beta lactamase inhibitor      2\n",
       "total not specified                                         2\n",
       "partial electrocautery                                      2\n",
       "preoperative nsaid                                          2\n",
       "total tonsillectomy - coblation                             1\n",
       "cpap                                                        1\n",
       "Name: Intervention_category, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data.Intervention_category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the surgery filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "surgery_data = clean_data[clean_data.Intervention_category.str.contains('total') \n",
    "                   | clean_data.Intervention_category.str.contains('partial')].dropna(subset=['outcome_obs']).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of interventions after filtering. Note that there were a lot of papers that did not report bleeding outcomes, so the numbers dropped significantly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total cold                                             74\n",
       "total electrocautery                                   71\n",
       "total coblation                                        34\n",
       "total harmonic scalpel                                 22\n",
       "total molecular resonance                              13\n",
       "partial coblation                                      13\n",
       "partial laser                                          12\n",
       "total thermal welding                                  10\n",
       "partial cold                                            7\n",
       "partial microdebrider                                   6\n",
       "total laser                                             6\n",
       "total tonsillectomy - unspecified                       5\n",
       "total argon plasma                                      3\n",
       "total adenotonsillectomy - unspecified + no surgery     3\n",
       "total adenotonsillectomy - unspecified                  1\n",
       "total not specified                                     1\n",
       "total microdebrider                                     1\n",
       "total tonsillectomy - coblation                         1\n",
       "partial electrocautery                                  1\n",
       "total plasmaknife                                       1\n",
       "total electrocautery + total cold                       1\n",
       "Name: Intervention_category, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surgery_data.Intervention_category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indicator for partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "surgery_data['partial'] = surgery_data.Intervention_category.str.contains('partial')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate column for technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "surgery_data['technique'] = (surgery_data.Intervention_category\n",
    "        .apply(lambda x: ' '.join(x.split(' ')[1:]))\n",
    "        .replace({'tonsillectomy - coblation':'coblation'}))\n",
    "\n",
    "surgery_data = surgery_data[~(surgery_data.technique.str.contains('specified')|\n",
    "                             surgery_data.technique.str.contains('\\+'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cold                   81\n",
       "electrocautery         72\n",
       "coblation              48\n",
       "harmonic scalpel       22\n",
       "laser                  18\n",
       "molecular resonance    13\n",
       "thermal welding        10\n",
       "microdebrider           7\n",
       "argon plasma            3\n",
       "plasmaknife             1\n",
       "Name: technique, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surgery_data.technique.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will drop microdebrider, argon plasma and plasma knife due to lack of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "technique_includes = surgery_data.technique.value_counts().index[:-3].tolist()\n",
    "surgery_data = surgery_data[surgery_data.technique.isin(technique_includes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cold',\n",
       " 'electrocautery',\n",
       " 'coblation',\n",
       " 'harmonic scalpel',\n",
       " 'laser',\n",
       " 'molecular resonance',\n",
       " 'thermal welding']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technique_includes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsets of data according to bleeding outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "readmission_bleeding_data = surgery_data[surgery_data.New_Bleeding_specify=='Revisit/Readmission-bleeding']\n",
    "reoperation_bleeding_data = surgery_data[surgery_data.New_Bleeding_specify=='Reoperation-bleeding']\n",
    "primary_bleeding_data = surgery_data[surgery_data.New_Bleeding_specify=='Primary bleeding']\n",
    "secondary_bleeding_data = surgery_data[surgery_data.New_Bleeding_specify=='Secondary bleeding']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymc3 import (NUTS, sample, Model, Deterministic, find_MAP,\n",
    "                  Binomial, Normal, HalfCauchy, advi,\n",
    "                  traceplot, summary, forestplot)\n",
    "import theano.tensor as tt\n",
    "\n",
    "def tinvlogit(x):\n",
    "    return tt.exp(x) / (1 + tt.exp(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def specify_model(model, data):\n",
    "    \n",
    "    N, REFID, events, partial = data[['N', 'REFID', 'outcome_obs', 'partial']].values.T\n",
    "    \n",
    "    refid_list = list(set(REFID))\n",
    "    study_id = data.REFID.apply(lambda i: refid_list.index(i)).values\n",
    "    n_studies = len(refid_list)\n",
    "    \n",
    "    technique = data.technique.apply(lambda i: technique_includes.index(i)).values\n",
    "    n_tech = len(technique_includes)\n",
    "    \n",
    "    assert not np.isnan(N.astype(int)).any()\n",
    "    assert not np.isnan(events.astype(int)).any()\n",
    "    \n",
    "    with model:\n",
    "        \n",
    "        # Mean for surgery interventions\n",
    "        θ = Normal('θ', 0, sd=5, shape=n_tech, testval=np.ones(n_tech)*-1)\n",
    "        # Effect of partial intervention\n",
    "        β = Normal('β', 0, sd=5, testval=0)\n",
    "\n",
    "        # Study random effect\n",
    "        σ = HalfCauchy('σ', 5, testval=1)\n",
    "        ϵ = Normal('ϵ', 0, sd=σ, shape=n_studies, testval=np.zeros(n_studies))\n",
    "\n",
    "        # Transform to probability scale\n",
    "        π = tinvlogit(θ[technique] + β*partial.astype(int) + ϵ[study_id])\n",
    "\n",
    "        # Mean probabilities\n",
    "        π_total = Deterministic('π_total', tinvlogit(θ))\n",
    "        π_partial = Deterministic('π_partial', tinvlogit(θ + β))\n",
    "\n",
    "        # Data likelihood\n",
    "        obs = Binomial('obs', N.astype(int), π, observed=events.astype(int))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied log-transform to σ and added transformed σ_log to model.\n"
     ]
    }
   ],
   "source": [
    "reoperation_model = specify_model(Model(), reoperation_bleeding_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model run using Hamiltonian Monte Carlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assigned NUTS to θ\n",
      "Assigned NUTS to β\n",
      "Assigned NUTS to σ_log\n",
      "Assigned NUTS to ϵ\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 122.3 sec"
     ]
    }
   ],
   "source": [
    "with reoperation_model:\n",
    "    reoperation_trace = sample(2000, random_seed=rseed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied log-transform to σ and added transformed σ_log to model.\n"
     ]
    }
   ],
   "source": [
    "readmission_model = specify_model(Model(), readmission_bleeding_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assigned NUTS to θ\n",
      "Assigned NUTS to β\n",
      "Assigned NUTS to σ_log\n",
      "Assigned NUTS to ϵ\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 131.3 sec"
     ]
    }
   ],
   "source": [
    "with readmission_model:\n",
    "    readmission_trace = sample(2000, random_seed=rseed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied log-transform to σ and added transformed σ_log to model.\n"
     ]
    }
   ],
   "source": [
    "primary_model = specify_model(Model(), primary_bleeding_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assigned NUTS to θ\n",
      "Assigned NUTS to β\n",
      "Assigned NUTS to σ_log\n",
      "Assigned NUTS to ϵ\n",
      " [-----------------100%-----------------] 2000 of 2000 complete in 189.9 sec"
     ]
    }
   ],
   "source": [
    "with primary_model:\n",
    "    primary_trace = sample(2000, random_seed=rseed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied log-transform to σ and added transformed σ_log to model.\n"
     ]
    }
   ],
   "source": [
    "secondary_model = specify_model(Model(), secondary_bleeding_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assigned NUTS to θ\n",
      "Assigned NUTS to β\n",
      "Assigned NUTS to σ_log\n",
      "Assigned NUTS to ϵ\n",
      " [-----------------100%-----------------] 2001 of 2000 complete in 114.4 sec"
     ]
    }
   ],
   "source": [
    "with secondary_model:\n",
    "    secondary_trace = sample(2000, random_seed=rseed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reoperation Bleeding Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posterior traces and histograms of surgical technique bleeding estimates for partial (top plot) and total (bottom plot) removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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33nmH4OBgQkNDGTBgANnZ2XTs2PGyA4pLderUieDgYKZPn05ycjJbtmxh5cqVGIbB/fff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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120291e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(reoperation_trace[1000:], varnames=['π_partial'], ylabels=technique_includes, \n",
    "           main='Partial removal', xtitle='Probability of re-operation bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZsoSlS5diGAZubm4cOXKEwMBAAKxWKxMnTnS0ZRgGALVr1wbgzJkzeHt74+vrC0BQUBDz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\nlebNmzN+/HjCwsL48ccfSUt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12028fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(reoperation_trace[1000:], varnames=['π_total'], ylabels=technique_includes, \n",
    "           main='Total removal', xtitle='Probability of re-operation bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "π_total:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.012            0.005            0.000            [0.003, 0.021]\n",
      "  0.011            0.004            0.000            [0.003, 0.019]\n",
      "  0.012            0.006            0.000            [0.003, 0.024]\n",
      "  0.039            0.014            0.001            [0.015, 0.066]\n",
      "  0.057            0.053            0.002            [0.002, 0.171]\n",
      "  0.008            0.010            0.000            [0.000, 0.027]\n",
      "  0.003            0.007            0.000            [0.000, 0.014]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.004          0.008          0.011          0.014          0.023\n",
      "  0.004          0.008          0.010          0.013          0.021\n",
      "  0.004          0.008          0.011          0.015          0.027\n",
      "  0.017          0.029          0.037          0.046          0.071\n",
      "  0.007          0.022          0.039          0.071          0.214\n",
      "  0.000          0.002          0.005          0.010          0.034\n",
      "  0.000          0.000          0.001          0.003          0.020\n",
      "\n",
      "\n",
      "π_partial:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.005            0.005            0.000            [0.000, 0.013]\n",
      "  0.005            0.005            0.000            [0.000, 0.013]\n",
      "  0.005            0.004            0.000            [0.000, 0.013]\n",
      "  0.016            0.015            0.000            [0.000, 0.045]\n",
      "  0.020            0.024            0.001            [0.000, 0.065]\n",
      "  0.003            0.006            0.000            [0.000, 0.013]\n",
      "  0.001            0.004            0.000            [0.000, 0.006]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.000          0.002          0.003          0.006          0.016\n",
      "  0.000          0.002          0.003          0.006          0.017\n",
      "  0.000          0.002          0.003          0.006          0.016\n",
      "  0.001          0.006          0.011          0.021          0.057\n",
      "  0.001          0.005          0.013          0.026          0.083\n",
      "  0.000          0.001          0.001          0.003          0.018\n",
      "  0.000          0.000          0.000          0.001          0.010\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "summary(reoperation_trace[1000:], varnames=['π_total', 'π_partial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Readmission Bleeding Results\n",
    "\n",
    "Posterior traces and histograms of surgical technique bleeding estimates for partial (top plot) and total (bottom plot) removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JCAigYcOG9OzZk/T0dNq0aZPtgOJ6bdu2JSAggMmTJxMXF8fmzZtZsWIFhmHw+OOP07Jl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\nmNT/A3bkRXz1+MCnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120fbaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(readmission_trace[1000:], varnames=['π_partial'], ylabels=technique_includes, \n",
    "           main='Partial removal', xtitle='Probability of re-admission bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fX2/i4i4USh8ikrXoaBc6dPDEZgOr1ZOIiCQFeT4qsiF+4MABtm/fzm+//QZAeno6Z8+e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\nNWoUf/zxBwA9evSwXwnugQceoGPHjix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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12119b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(readmission_trace[1000:], varnames=['π_total'], ylabels=technique_includes, \n",
    "           main='Total removal', xtitle='Probability of re-admission bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "π_total:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.028            0.012            0.001            [0.008, 0.053]\n",
      "  0.027            0.012            0.001            [0.006, 0.052]\n",
      "  0.014            0.012            0.000            [0.001, 0.036]\n",
      "  0.015            0.008            0.000            [0.003, 0.030]\n",
      "  0.060            0.042            0.001            [0.008, 0.140]\n",
      "  0.000            0.001            0.000            [0.000, 0.002]\n",
      "  0.027            0.067            0.002            [0.000, 0.141]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.010          0.019          0.027          0.035          0.058\n",
      "  0.009          0.018          0.026          0.035          0.058\n",
      "  0.002          0.006          0.011          0.018          0.047\n",
      "  0.004          0.009          0.014          0.019          0.034\n",
      "  0.012          0.032          0.050          0.076          0.162\n",
      "  0.000          0.000          0.000          0.001          0.003\n",
      "  0.000          0.000          0.004          0.021          0.221\n",
      "\n",
      "\n",
      "π_partial:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.041            0.042            0.001            [0.001, 0.124]\n",
      "  0.040            0.042            0.001            [0.001, 0.122]\n",
      "  0.014            0.009            0.000            [0.001, 0.032]\n",
      "  0.022            0.026            0.001            [0.000, 0.067]\n",
      "  0.076            0.075            0.003            [0.001, 0.235]\n",
      "  0.001            0.002            0.000            [0.000, 0.003]\n",
      "  0.036            0.093            0.003            [0.000, 0.192]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.004          0.014          0.028          0.050          0.162\n",
      "  0.004          0.013          0.027          0.050          0.165\n",
      "  0.003          0.007          0.012          0.018          0.040\n",
      "  0.002          0.007          0.014          0.027          0.090\n",
      "  0.008          0.028          0.052          0.096          0.292\n",
      "  0.000          0.000          0.000          0.001          0.005\n",
      "  0.000          0.000          0.004          0.024          0.316\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "summary(readmission_trace[1000:], varnames=['π_total', 'π_partial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primary Bleeding Results\n",
    "\n",
    "Posterior traces and histograms of surgical technique bleeding estimates for partial (top plot) and total (bottom plot) removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K/PnzCQ0NpX79+nTv3p3s7Gxatmx50QXFhVq1akVoaCjjx48nKSmJr776ioULF2IYBg8/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12151c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(primary_trace[1000:], varnames=['π_partial'], ylabels=technique_includes, \n",
    "           main='Partial removal', xtitle='Probability of primary bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bzcj1EbcZRrmFnTE+Pm/TaV3mHzPkdLYSym7/FBRkxWb7Y78QFJRMfHzRj8Sd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\nFVlZWTRt2pSRI0cCsG7dOj755BMMw3B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1216b9a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(primary_trace[1000:], varnames=['π_total'], ylabels=technique_includes, \n",
    "           main='Total removal', xtitle='Probability of primary bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "π_total:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.007            0.004            0.000            [0.002, 0.015]\n",
      "  0.005            0.003            0.000            [0.001, 0.011]\n",
      "  0.013            0.009            0.000            [0.001, 0.031]\n",
      "  0.009            0.009            0.000            [0.000, 0.026]\n",
      "  0.011            0.036            0.001            [0.000, 0.053]\n",
      "  0.004            0.011            0.000            [0.000, 0.016]\n",
      "  0.005            0.018            0.000            [0.000, 0.020]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.002          0.005          0.007          0.009          0.017\n",
      "  0.001          0.003          0.005          0.007          0.012\n",
      "  0.002          0.007          0.011          0.017          0.036\n",
      "  0.001          0.004          0.007          0.013          0.031\n",
      "  0.000          0.000          0.001          0.007          0.086\n",
      "  0.000          0.000          0.001          0.003          0.028\n",
      "  0.000          0.000          0.001          0.004          0.031\n",
      "\n",
      "\n",
      "π_partial:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.016            0.018            0.001            [0.001, 0.051]\n",
      "  0.012            0.016            0.001            [0.000, 0.041]\n",
      "  0.021            0.017            0.000            [0.001, 0.053]\n",
      "  0.022            0.034            0.001            [0.000, 0.082]\n",
      "  0.014            0.045            0.001            [0.000, 0.067]\n",
      "  0.009            0.028            0.001            [0.000, 0.036]\n",
      "  0.011            0.039            0.001            [0.000, 0.043]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.001          0.005          0.010          0.020          0.064\n",
      "  0.001          0.003          0.007          0.015          0.054\n",
      "  0.003          0.010          0.017          0.028          0.064\n",
      "  0.001          0.004          0.010          0.024          0.120\n",
      "  0.000          0.000          0.002          0.010          0.091\n",
      "  0.000          0.000          0.001          0.006          0.066\n",
      "  0.000          0.000          0.001          0.007          0.082\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "summary(primary_trace[1000:], varnames=['π_total', 'π_partial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Secondary Bleeding Results\n",
    "\n",
    "Posterior traces and histograms of surgical technique bleeding estimates for partial (top plot) and total (bottom plot) removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/PwcOHODVV1/lyJEjxMXFndPvr7/+imEYZGZm8scff1CvXj0aNGjAyy+/zNVX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\nERExqf8D3OuSd1I+k1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121c1f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(secondary_trace[1000:], varnames=['π_partial'], ylabels=technique_includes, \n",
    "           main='Partial removal', xtitle='Probability of secondary bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4h+hoKx07+mC3g83mQ0REkoI8DxWuo881Dh48yO7du/n5558BSE9PJyEhwXnf/PnzWbhw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\nHTt2ZNOmTcDV34YODw93rvPa29avX8/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120afe048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forestplot(secondary_trace[1000:], varnames=['π_total'], ylabels=technique_includes, \n",
    "           main='Total removal', xtitle='Probability of secondary bleeding');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "π_total:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.032            0.008            0.000            [0.019, 0.047]\n",
      "  0.036            0.009            0.000            [0.020, 0.054]\n",
      "  0.019            0.008            0.000            [0.006, 0.034]\n",
      "  0.032            0.012            0.000            [0.012, 0.057]\n",
      "  0.012            0.012            0.000            [0.000, 0.035]\n",
      "  0.006            0.004            0.000            [0.000, 0.014]\n",
      "  0.037            0.021            0.001            [0.008, 0.079]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.019          0.026          0.031          0.036          0.049\n",
      "  0.021          0.030          0.035          0.041          0.056\n",
      "  0.007          0.013          0.018          0.023          0.038\n",
      "  0.013          0.023          0.031          0.039          0.060\n",
      "  0.001          0.004          0.008          0.016          0.047\n",
      "  0.001          0.003          0.005          0.008          0.017\n",
      "  0.011          0.022          0.032          0.047          0.093\n",
      "\n",
      "\n",
      "π_partial:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.008            0.010            0.000            [0.000, 0.028]\n",
      "  0.010            0.012            0.001            [0.000, 0.032]\n",
      "  0.004            0.005            0.000            [0.000, 0.013]\n",
      "  0.008            0.010            0.000            [0.000, 0.030]\n",
      "  0.003            0.005            0.000            [0.000, 0.012]\n",
      "  0.002            0.003            0.000            [0.000, 0.006]\n",
      "  0.010            0.013            0.001            [0.000, 0.036]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.000          0.002          0.005          0.011          0.037\n",
      "  0.000          0.003          0.006          0.012          0.046\n",
      "  0.000          0.001          0.003          0.006          0.017\n",
      "  0.000          0.002          0.005          0.010          0.039\n",
      "  0.000          0.000          0.001          0.003          0.016\n",
      "  0.000          0.000          0.001          0.002          0.008\n",
      "  0.000          0.002          0.005          0.012          0.049\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "summary(secondary_trace[1000:], varnames=['π_total', 'π_partial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goodness of fit\n",
    "\n",
    "Plots of Bayesian p-values. An excess of very large or very small values may suggest lack of fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pymc3 import sample_ppc\n",
    "\n",
    "ppc = [sample_ppc(trace, model=model, samples=500) for trace,model in ([reoperation_trace, reoperation_model],\n",
    "                                                                      [readmission_trace, readmission_model],\n",
    "                                                                      [primary_trace, primary_model],\n",
    "                                                                      [secondary_trace, secondary_model])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bayes_p = lambda sim, true: [(s > t).mean() for s,t in zip(sim, true)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obs_sim = [p['obs'] for p in ppc]\n",
    "p_values = [bayes_p(simdata, model.obs.tag.test_value) for simdata,model in zip(obs_sim, \n",
    "                                        [reoperation_model, readmission_model, primary_model, secondary_model])]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9egAAAgICMG7cOHz88ceYP38+du/ejb/+9a9KrrZ7DhuwQuqKKy4uDqmpqUhL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\nDbABExERaYANmIiISAP/D0zzK7JDw5uqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123dd72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfp = pd.DataFrame(p_values).T\n",
    "dfp.columns = 'Reoperation', 'Readmission', 'Primary', 'Secondary'\n",
    "dfp.hist(sharex=True, bins=15);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}