ICU.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:4c0319be185104535bde2911cce30e44d6f561c8f4ed0ba1c06d965bb66fc6fd"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized = pd.read_csv('data/hospitalized.csv', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/io/parsers.py:1071: DtypeWarning: Columns (140,142,144,146,148,181,206,207,212,213,261,280,281,282,296,297) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Proportion admitted to ICU"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.dir_icu.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0.071315872514988957"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.dir_icu.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "226"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Number and proportion in ICU at any time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['icu_any'] = (hospitalized.icu + hospitalized.dir_icu).astype(bool)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.icu_any.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "312"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.icu_any.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "0.098453770905648469"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create subset of cases that were admitted to ICU later"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['icu_later'] = hospitalized.icu_any & (hospitalized.dir_icu.astype(bool) - True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Subset who were admitted directly to ICU"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir_icu_subset = hospitalized[hospitalized.dir_icu.astype(bool)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Age distribution of this subset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir_icu_subset.age_months.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x103d12050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAECCAYAAADw0Rw8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105ecd650>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the subset admitted directly to ICU, what proportion were <1 month old"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.age_months<1).mean()\n",
      "(hospitalized.age_months<1).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "0.12212054275796781"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Proportion <1 month in entire dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.age_months<1).mean()\n",
      "(hospitalized.age_months<1).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "0.12212054275796781"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Gestational age of direct-to-ICU subset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.gest_age<37).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "0.19026548672566371"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How many days sick before hospitalization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir_icu_subset[dir_icu_subset.days_symptoms<10].boxplot(column='days_symptoms')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x103880290>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x105ce3290>,\n",
        "  <matplotlib.lines.Line2D at 0x105ce38d0>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x106062550>],\n",
        " 'means': [],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x105ce3f10>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x105ce5550>,\n",
        "  <matplotlib.lines.Line2D at 0x105ce5c10>]}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD8CAYAAACFK0QrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105d908d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Birth weight of entire hospitalized dataset (red) and direct-to-ICU subset (blue):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.birth_wt_child.hist(color='red', normed=True)\n",
      "dir_icu_subset.birth_wt_child.hist(color='blue', normed=True, alpha=0.8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x106073e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAECCAYAAAAb5qc/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTIxFnojIxP4XVKWAblL3UtsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106069390>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "z-scores less than -1 for direct-to-ICU and all hospitalizations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.z_score < -1).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0.47345132743362833"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(hospitalized.z_score < -1).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "0.26001893341748183"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "More than 2 sd below mean:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.z_score < -2).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0.32300884955752213"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(hospitalized.z_score < -2).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0.13568949195329758"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Days in ICU of all (red) and direct-to-ICU (blue):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized.length_of_stay.hist(bins=30, normed=True, color='darkred')\n",
      "dir_icu_subset.length_of_stay.hist(bins=30, normed=True, alpha=0.75)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x104f47e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAECCAYAAAASDQdFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106069690>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Death in ICU for direct-to-ICU subset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(dir_icu_subset.death).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "0.06637168141592921"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Length of stay for direct-to-ICU subset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir_icu_subset[dir_icu_subset.death].length_of_stay.hist(bins=25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x105fd8490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAECCAYAAADelD2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPMPgBoA8w+AGgDzD4AaAPPN/TcSllkie18cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105fdc050>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract ICU subset\n",
      "icu_subset = hospitalized[hospitalized.icu_any]\n",
      "icu_subset['icu_direct'] = icu_subset.dir_icu.replace({0:'Later', 1:'Direct'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Group by direct to ICU or not\n",
      "by_direct = icu_subset.groupby('icu_direct')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Days on ventilator by whether patient was sent direct to ICU or later during hospitalization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_direct['days_vent'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "icu_direct       \n",
        "Direct      count    34.000000\n",
        "            mean      4.617647\n",
        "            std       4.431394\n",
        "            min       1.000000\n",
        "            25%       2.000000\n",
        "            50%       3.000000\n",
        "            75%       6.750000\n",
        "            max      20.000000\n",
        "Later       count    17.000000\n",
        "            mean      3.941176\n",
        "            std       3.543843\n",
        "            min       1.000000\n",
        "            25%       1.000000\n",
        "            50%       2.000000\n",
        "            75%       5.000000\n",
        "            max      10.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Length of stay"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_direct['length_of_stay'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "icu_direct       \n",
        "Direct      count    222.000000\n",
        "            mean       9.108108\n",
        "            std        5.280120\n",
        "            min        2.000000\n",
        "            25%        6.000000\n",
        "            50%        8.000000\n",
        "            75%       11.000000\n",
        "            max       42.000000\n",
        "Later       count     61.000000\n",
        "            mean       8.327869\n",
        "            std        6.166364\n",
        "            min        1.000000\n",
        "            25%        4.000000\n",
        "            50%        7.000000\n",
        "            75%       10.000000\n",
        "            max       33.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "z-score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_direct['z_score'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "icu_direct       \n",
        "Direct      count    225.000000\n",
        "            mean      -1.198444\n",
        "            std        2.964529\n",
        "            min      -11.190000\n",
        "            25%       -2.790000\n",
        "            50%       -0.800000\n",
        "            75%        0.570000\n",
        "            max       25.330000\n",
        "Later       count     84.000000\n",
        "            mean      -1.163452\n",
        "            std        2.333833\n",
        "            min       -7.250000\n",
        "            25%       -2.335000\n",
        "            50%       -0.840000\n",
        "            75%        0.490000\n",
        "            max        3.810000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Age distribution of those sent directly to ICU versus later"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "icu_subset['age_months'].hist(by=icu_subset['icu_direct'], sharey=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([<matplotlib.axes._subplots.AxesSubplot object at 0x106b37b10>,\n",
        "       <matplotlib.axes._subplots.AxesSubplot object at 0x106d2aad0>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAE8CAYAAACB5saFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9AISAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106be6390>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Predictive model for ICU admission"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized[\"prev_cond\"] = hospitalized[[c for c in hospitalized.columns if c.endswith('hx') and not c.startswith('no_')]].sum(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables = hospitalized[[\"icu_any\",\n",
      "                            \"hospitalized_vitamin_d\", \n",
      "                            \"gest_age\", \n",
      "                            \"heart_hx\",\n",
      "                            \"prev_cond\",\n",
      "                            \"cigarette_smokers\",\n",
      "                            \"sex_child\",\n",
      "                            \"birth_wt_child\",\n",
      "                            \"z_score\",\n",
      "                            \"age_months\",\n",
      "                            \"wheezing\"\n",
      "                           ]]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables['vitamin_d_norm'] = ((variables.hospitalized_vitamin_d - variables.hospitalized_vitamin_d.mean()) \n",
      "                               / variables.hospitalized_vitamin_d.std())\n",
      "\n",
      "variables['wt_norm'] = ((variables.birth_wt_child - variables.birth_wt_child.mean()) /\n",
      "                        variables.birth_wt_child.std())\n",
      "\n",
      "variables['age_norm'] = ((variables.age_months - variables.age_months.mean()) /\n",
      "                        variables.age_months.std())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Observations with missing values, by variable"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables.isnull().sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "icu_any                     0\n",
        "hospitalized_vitamin_d    480\n",
        "gest_age                    0\n",
        "heart_hx                    0\n",
        "prev_cond                   0\n",
        "cigarette_smokers           1\n",
        "sex_child                   0\n",
        "birth_wt_child              2\n",
        "z_score                    13\n",
        "age_months                  0\n",
        "wheezing                    0\n",
        "vitamin_d_norm            480\n",
        "wt_norm                     2\n",
        "age_norm                    0\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables = variables.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variables.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "(2677, 14)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_glm = variables.to_dict(outtype='list')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymc import Model, glm\n",
      "import theano.tensor as t\n",
      "from pymc import sample, Slice, NUTS\n",
      "from pymc import forestplot, traceplot, summary\n",
      "\n",
      "def tinvlogit(x):\n",
      "    return t.exp(x) / (1 + t.exp(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with Model() as model:\n",
      "    \n",
      "    formula = 'icu_any ~ vitamin_d_norm + cigarette_smokers + sex_child + prev_cond + z_score + age_norm'\n",
      "    \n",
      "    glm.glm(formula, data_glm,\n",
      "            family=glm.families.Binomial(link=glm.links.Logit))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with model:\n",
      "    trace = sample(5000, Slice())[4000:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " [-----------------100%-----------------] 5000 of 5000 complete in 64.5 sec"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(14,8))\n",
      "forestplot(trace, vars=trace.varnames[1:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<matplotlib.gridspec.GridSpec at 0x11c778e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAysAAAIXCAYAAACcmME+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAHAlhhUAAAAArsSwAgAAAMCV/gODcJYUbgMnpAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c778110>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "traceplot(trace, vars=['z_score'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAACKCAYAAABYQ4AvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wcjxqVqAooXYgAAAABJRU5ErkJggg==\n",
       "prompt_number": 58,
       "text": [
        "<matplotlib.figure.Figure at 0x11c8eff90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAACKCAYAAABYQ4AvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wcjxqVqAooXYgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11c8eff90>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vitamin D levels for ICU vs non-ICU"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hospitalized['hospitalized_vitamin_d'].hist(by=hospitalized['icu_any'], sharey=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([<matplotlib.axes._subplots.AxesSubplot object at 0x108aa3bd0>,\n",
        "       <matplotlib.axes._subplots.AxesSubplot object at 0x107f34190>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAE8CAYAAACfL/Y2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI6wBAAAYjLAGAABgMMIaAACAwf4fCZ8oChnUzGoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107e0ba50>"
       ]
      }
     ],
     "prompt_number": 43
    }
   ],
   "metadata": {}
  }
 ]
}