fonnesbeck/Tonsillectomy KQ2.ipynb

## Tonsillectomy KQ2.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\n\nimport re",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data_file = 'data/Tonsillectomy_KQ2_data_For_MA_07202016.xlsx'\n\nthroat_data = (pd.read_excel(data_file, \n              sheetname='Throat infections_KQ2', \n              na_values=['null', 'ND'])\n            .drop(['Comments', 'Comments 2'], \n                  axis=1))\nthroat_data.head(2)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Number_of_Arms</th>\n      <th>Rx_Grouping</th>\n      <th>Group_Desc</th>\n      <th>Intervention_category</th>\n      <th>OUTC_Main_\nCATG</th>\n      <th>Outc_SUB_\nCATG</th>\n      <th>Outcome_specify</th>\n      <th>Outc_Unit</th>\n      <th>...</th>\n      <th>Outcome_count</th>\n      <th>Outcome_%</th>\n      <th>Outcome_Mean</th>\n      <th>Outcome_SD</th>\n      <th>Outcome_ Median</th>\n      <th>Outcome_Min</th>\n      <th>Outcome_Max</th>\n      <th>Outcome_95%L</th>\n      <th>Outcome_95%H</th>\n      <th>Results</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>L. J. Orvidas, J. L. St Sauver and A. L. Weave...</td>\n      <td>2746</td>\n      <td>2</td>\n      <td>G1:</td>\n      <td>Tonsillectomy</td>\n      <td>Total tonsillectomy - unspecified</td>\n      <td>Throat infection</td>\n      <td>Throat infection-# strep infections</td>\n      <td>Group A Beta-strep throat infections/year</td>\n      <td>number of episodes/year</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>13.2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.5</td>\n      <td>18.6</td>\n      <td>number still at risk=124</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>L. J. Orvidas, J. L. St Sauver and A. L. Weave...</td>\n      <td>2746</td>\n      <td>2</td>\n      <td>G2:</td>\n      <td>No surgery</td>\n      <td>No surgery</td>\n      <td>Throat infection</td>\n      <td>Throat infection-# strep infections</td>\n      <td>Group A Beta-strep throat infections/year</td>\n      <td>number of episodes/year</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>39.3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>30.8</td>\n      <td>46.8</td>\n      <td>number still at risk=87</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 30 columns</p>\n</div>",
            "text/plain": "                                            Citation REFID  Number_of_Arms  \\\n0  L. J. Orvidas, J. L. St Sauver and A. L. Weave...  2746               2   \n1  L. J. Orvidas, J. L. St Sauver and A. L. Weave...  2746               2   \n\n  Rx_Grouping     Group_Desc              Intervention_category  \\\n0         G1:  Tonsillectomy  Total tonsillectomy - unspecified   \n1         G2:     No surgery                         No surgery   \n\n   OUTC_Main_\\nCATG                      Outc_SUB_\\nCATG  \\\n0  Throat infection  Throat infection-# strep infections   \n1  Throat infection  Throat infection-# strep infections   \n\n                             Outcome_specify                Outc_Unit  \\\n0  Group A Beta-strep throat infections/year  number of episodes/year   \n1  Group A Beta-strep throat infections/year  number of episodes/year   \n\n             ...             Outcome_count Outcome_% Outcome_Mean Outcome_SD  \\\n0            ...                       NaN      13.2          NaN        NaN   \n1            ...                       NaN      39.3          NaN        NaN   \n\n  Outcome_ Median  Outcome_Min  Outcome_Max  Outcome_95%L Outcome_95%H  \\\n0             NaN          NaN          NaN           7.5         18.6   \n1             NaN          NaN          NaN          30.8         46.8   \n\n                    Results  \n0  number still at risk=124  \n1   number still at risk=87  \n\n[2 rows x 30 columns]"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "school_data = (pd.read_excel(data_file, \n              sheetname='Missed_School_KQ2', \n              na_values=['null', 'ND']))\nschool_data.head(2)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Number_of_Arms</th>\n      <th>Rx_Grouping</th>\n      <th>Group_Desc</th>\n      <th>Intervention_category</th>\n      <th>OUTC_Main_\nCATG</th>\n      <th>Outc_SUB_\nCATG</th>\n      <th>Outcome_specify</th>\n      <th>Outc_Unit</th>\n      <th>...</th>\n      <th>BL Min</th>\n      <th>BL Max</th>\n      <th>BL 95% L</th>\n      <th>BL 95% H</th>\n      <th>Outcome_timepoint</th>\n      <th>OUTCOME_SAMPLE_SIZE</th>\n      <th>Outcome_count</th>\n      <th>Outcome_ %</th>\n      <th>Outcome_ Mean</th>\n      <th>Outcome_SD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C. Lock, J. Wilson, N. Steen, M. Eccles, H. Ma...</td>\n      <td>1861_1377_1940 nRCT</td>\n      <td>2</td>\n      <td>G1:</td>\n      <td>dissection or bipolar diathermy tonsillectomy</td>\n      <td>Total tonsillectomy - dissection/bipolar tonsi...</td>\n      <td>Missed school</td>\n      <td>Missed school</td>\n      <td>number of school days missed</td>\n      <td>days</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2 yrs post-op</td>\n      <td>187</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11.2</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C. Lock, J. Wilson, N. Steen, M. Eccles, H. Ma...</td>\n      <td>1861_1377_1940 nRCT</td>\n      <td>2</td>\n      <td>G2:</td>\n      <td>medical treatment</td>\n      <td>conventional medical treatment</td>\n      <td>Missed school</td>\n      <td>Missed school</td>\n      <td>number of school days missed</td>\n      <td>days</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2 yrs post-op</td>\n      <td>52</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.6</td>\n      <td>6.4</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 30 columns</p>\n</div>",
            "text/plain": "                                            Citation                REFID  \\\n0  C. Lock, J. Wilson, N. Steen, M. Eccles, H. Ma...  1861_1377_1940 nRCT   \n1  C. Lock, J. Wilson, N. Steen, M. Eccles, H. Ma...  1861_1377_1940 nRCT   \n\n   Number_of_Arms Rx_Grouping                                      Group_Desc  \\\n0               2         G1:  dissection or bipolar diathermy tonsillectomy    \n1               2         G2:                               medical treatment   \n\n                               Intervention_category OUTC_Main_\\nCATG  \\\n0  Total tonsillectomy - dissection/bipolar tonsi...    Missed school   \n1                     conventional medical treatment    Missed school   \n\n  Outc_SUB_\\nCATG               Outcome_specify Outc_Unit     ...     BL Min  \\\n0   Missed school  number of school days missed      days     ...        NaN   \n1   Missed school  number of school days missed      days     ...        NaN   \n\n   BL Max BL 95% L BL 95% H  Outcome_timepoint  OUTCOME_SAMPLE_SIZE  \\\n0     NaN      NaN      NaN      2 yrs post-op                  187   \n1     NaN      NaN      NaN      2 yrs post-op                   52   \n\n   Outcome_count  Outcome_ %  Outcome_ Mean  Outcome_SD  \n0            NaN         NaN           11.2         9.0  \n1            NaN         NaN            6.6         6.4  \n\n[2 rows x 30 columns]"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Throat infection model"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "excludes = '2746'\n\nthroat_data",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "throat_data.Group_Desc.str.lower().replace({'no surgery':'control', \n                                            'watchful waiting':'control'}).value_counts()",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "control                                           33\nadenotonsillectomy                                28\ntonsillectomy                                     17\ndissection or bipolar diathermy tonsillectomy      8\nmedical treatment                                  8\nName: Group_Desc, dtype: int64"
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "throat_data.Group_Desc.value_counts()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Adenotonsillectomy                                28\nNo Surgery                                        24\nTonsillectomy                                     17\nmedical treatment                                  8\ndissection or bipolar diathermy tonsillectomy      8\nNo surgery                                         5\nWatchful Waiting                                   4\nName: Group_Desc, dtype: int64"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## School days model"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "school_data.Group_Desc.value_counts()",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Adenotonsillectomy                                6\nNo Surgery                                        6\nTonsillectomy                                     3\nmedical treatment                                 1\ndissection or bipolar diathermy tonsillectomy     1\nName: Group_Desc, dtype: int64"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Treatment group includes all surgical interventions, otherwise control"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "school_data['treatment'] = school_data.Group_Desc.str.contains('tonsil').astype(int)",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Only three studies"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "school_data.REFID.unique()",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array(['1861_1377_1940 nRCT', '3641_a', '3641_b'], dtype=object)"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "school_data.columns",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['Citation', 'REFID', 'Number_of_Arms', 'Rx_Grouping', 'Group_Desc',\n       'Intervention_category', 'OUTC_Main_\\nCATG', 'Outc_SUB_\\nCATG',\n       'Outcome_specify', 'Outc_Unit', 'Outc_Tool', 'BASELINE SAMPLE SIZE',\n       'BL_Count', 'BL %', 'BL Mean', 'BL SD', 'BL SE', 'BL_Median', 'BL_Q1',\n       'BL_Q3', 'BL Min', 'BL Max', 'BL 95% L', 'BL 95% H',\n       'Outcome_timepoint', 'OUTCOME_SAMPLE_SIZE', 'Outcome_count',\n       'Outcome_ %', 'Outcome_ Mean', 'Outcome_SD', 'treatment'],\n      dtype='object')"
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "school_analysis_subset = school_data[['REFID', 'treatment', \n                            'OUTCOME_SAMPLE_SIZE', 'Outcome_ Mean', 'Outcome_SD']]\nschool_analysis_subset.isnull().sum()",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "REFID                  0\ntreatment              0\nOUTCOME_SAMPLE_SIZE    0\nOutcome_ Mean          0\nOutcome_SD             0\ndtype: int64"
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "from pymc3 import Model, Normal, sample, forestplot, traceplot, summary, sample_ppc\n\nwith Model() as school_days_model:\n    \n    x, y, s = school_analysis_subset[['treatment', 'Outcome_ Mean', 'Outcome_SD']].T.values\n    \n    μ = Normal('μ', 0, sd=100)\n    δ = Normal('δ', 0, sd=100)\n    \n    θ = μ + δ*x\n    \n    data_likelihood = Normal('data_likelihood', θ, sd=s, observed=y)",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "with school_days_model:\n    \n    school_trace = sample(2000, njobs=2)",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Assigned NUTS to μ\nAssigned NUTS to δ\n [-----------------100%-----------------] 2000 of 2000 complete in 1.9 sec",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "traceplot(school_trace[1000:], varnames=['μ', 'δ'])",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11a35d5f8>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x1197ce240>],\n       [<matplotlib.axes._subplots.AxesSubplot object at 0x1196f9828>,\n        <matplotlib.axes._subplots.AxesSubplot object at 0x11a365b70>]], dtype=object)"
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7/vEi8bwnYyIWpmTYmLbXv+24tKW8ECUnppNqizMynifV2k04oSKiJq2xKCEl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y8W+Xx+qsWZEF/96lfF9773PSGEEHv27BHve9/7plii8fHrX/9a/K//9b+EEEJs2rRJ3H777VMs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fGzM8DofHh1HVfJu7K8hqM6hqeOu4s/3KJQlCE/q++n4xnx0uw2zU8uCd4xiX1Xr/YyEEhyuK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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x11a299f60>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Treatment effect on school days missed"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "summary(school_trace[1000:], varnames=['δ'])",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nδ:\n\n  Mean             SD               MC Error         95% HPD interval\n  -------------------------------------------------------------------\n  \n  -0.655           1.999            0.080            [-4.425, 3.274]\n\n  Posterior quantiles:\n  2.5            25             50             75             97.5\n  |--------------|==============|==============|--------------|\n  \n  -4.575         -2.007         -0.621         0.677          3.167\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Goodness of fit"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "school_ppc = sample_ppc(school_trace, model=school_days_model, samples=500)",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "bayes_p = lambda sim, true: [(s > t).mean() for s,t in zip(sim, true)]\n\nsim_days = school_ppc['data_likelihood']\nsim_days.shape",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(500, 17)"
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Bayesian p-values for posterior predictive checks"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "(sim_days < y).mean(0)",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 0.81 ,  0.616,  0.548,  0.446,  0.606,  0.532,  0.656,  0.378,\n        0.42 ,  0.548,  0.518,  0.58 ,  0.442,  0.334,  0.462,  0.404,\n        0.52 ])"
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "anaconda-cloud": {},
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "name": "python",
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "mimetype": "text/x-python",
      "version": "3.5.2"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Tonsillectomy KQ2.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}