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fonnesbeck/Disruptive Behavior Disorder Meta-analysis.ipynb

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Disruptive behavior disorder meta-analysis (RCT only)
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Disruptive Behavior Disorder Meta-analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sb\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc as pm\n",
"from scipy.misc import comb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data import and cleaning"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"excel_sucks = True\n",
"\n",
"if excel_sucks:\n",
"\n",
" study_char = pd.read_csv(\"study_characteristics.csv\", \n",
" index_col='RefID', na_values=['-', 'NR'])\n",
" outcomes = pd.read_csv(\"outcomes.csv\", na_values=['ND', 'NR'])\n",
" \n",
"else:\n",
" \n",
" data_file = 'DBD Data for Meta Analyses_4-7-15_withNRCTs_RAE.xlsx'\n",
"\n",
" study_char = pd.read_excel(data_file, \"Study Characteristics\", \n",
" index_col='RefID', na_values=['-', 'NR'])\n",
" outcomes = pd.read_excel(data_file, \"Outcomes\", \n",
" na_values=['ND', 'NR'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pubmed ID</th>\n",
" <th>Rel Incl1</th>\n",
" <th>Rel Incl2</th>\n",
" <th>Rel Incl3</th>\n",
" <th>Rel Excl</th>\n",
" <th>Family</th>\n",
" <th>NCT</th>\n",
" <th>Title</th>\n",
" <th>Year</th>\n",
" <th>Author</th>\n",
" <th>...</th>\n",
" <th>Screened (N)</th>\n",
" <th>Randomized (N)</th>\n",
" <th>Analyzed (N)</th>\n",
" <th>Proportion Male (%)</th>\n",
" <th>Direction of Effect</th>\n",
" <th>DOE_Comments</th>\n",
" <th>Other comments</th>\n",
" <th>Drugs(s)</th>\n",
" <th>-</th>\n",
" <th>Unnamed: 46</th>\n",
" </tr>\n",
" <tr>\n",
" <th>RefID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th> 8385</th>\n",
" <td> na</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> Reducing Adolescent Oppositional and Conduct D...</td>\n",
" <td> 2011</td>\n",
" <td> Sells SP, K. W. Early and T. E. Smith</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td> 38</td>\n",
" <td>NaN</td>\n",
" <td> 57</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> 8401</th>\n",
" <td> 22820873</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> \"Tuning into Kids\": reducing young children's ...</td>\n",
" <td> 2013</td>\n",
" <td> Havighurst SS, K. R. Wilson, A. E. Harley, C. ...</td>\n",
" <td>...</td>\n",
" <td> 92</td>\n",
" <td> 63</td>\n",
" <td> 54</td>\n",
" <td> 78</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>NaN </th>\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",
" <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>NaN </th>\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",
" <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>NaN </th>\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",
" <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 × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" Pubmed ID Rel Incl1 Rel Incl2 Rel Incl3 Rel Excl Family NCT \\\n",
"RefID \n",
" 8385 na NaN NaN NaN NaN NaN NaN \n",
" 8401 22820873 NaN NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" Title Year \\\n",
"RefID \n",
" 8385 Reducing Adolescent Oppositional and Conduct D... 2011 \n",
" 8401 \"Tuning into Kids\": reducing young children's ... 2013 \n",
"NaN NaN NaN \n",
"NaN NaN NaN \n",
"NaN NaN NaN \n",
"\n",
" Author ... \\\n",
"RefID ... \n",
" 8385 Sells SP, K. W. Early and T. E. Smith ... \n",
" 8401 Havighurst SS, K. R. Wilson, A. E. Harley, C. ... ... \n",
"NaN NaN ... \n",
"NaN NaN ... \n",
"NaN NaN ... \n",
"\n",
" Screened (N) Randomized (N) Analyzed (N) Proportion Male (%) \\\n",
"RefID \n",
" 8385 NaN 38 NaN 57 \n",
" 8401 92 63 54 78 \n",
"NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN \n",
"\n",
" Direction of Effect DOE_Comments Other comments Drugs(s) - \\\n",
"RefID \n",
" 8385 NaN NaN NaN NaN NaN \n",
" 8401 NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN \n",
"NaN NaN NaN NaN NaN NaN \n",
"\n",
" Unnamed: 46 \n",
"RefID \n",
" 8385 NaN \n",
" 8401 NaN \n",
"NaN NaN \n",
"NaN NaN \n",
"NaN NaN \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"study_char.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data cleaning"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Cast outcomes variables to floats\n",
"for col in ('Last FU Mean', 'Last FU SD',):\n",
" outcomes[col] = outcomes[col].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Recode age category\n",
"study_char['age_cat'] = study_char.AgeCat.replace({'PRE-K':1, 'SCHOOL':0, 'TEEN':2})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Fix data label typo\n",
"outcomes['Measure Instrument'] = outcomes['Measure Instrument'].replace({'Eyberg Child Behaviour Inventory, problem Subscale': \n",
" 'Eyberg Child Behaviour Inventory, Problem Subscale'})\n",
"outcomes.Units = outcomes.Units.replace({'scale': 'Scale'})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Parse followup times and convert to months\n",
"split_fut = outcomes.loc[outcomes['Last FU Time'].notnull(), 'Last FU Time'].apply(lambda x: str(x).split(' ')[:2])\n",
"fut_months = [float(time)/52.*(unit=='weeks') or float(time) for time, unit in split_fut]\n",
"outcomes.loc[outcomes['Last FU Time'].notnull(), 'Last FU Time'] = fut_months"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are assumung all CBC Externalizing values over 50 are T-scores, and those under 50 are raw scores. This recodes those observations."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cbce_ind = outcomes['Measure Instrument'].apply(lambda x: x.startswith('Child Behavior Checklist, Externalizing'))\n",
"under_50 = outcomes['BL Mean']<50\n",
"outcomes.loc[cbce_ind & (under_50^True), 'Measure Instrument'] = 'Child Behavior Checklist, Externalizing (T Score)'\n",
"outcomes.loc[cbce_ind & under_50, 'Measure Instrument'] = 'Child Behavior Checklist, Externalizing'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode measure instrument variables"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"instrument = []\n",
"subtype = []\n",
"units = []\n",
"\n",
"for i,row in outcomes.iterrows():\n",
" separator = row['Measure Instrument'].find(',')\n",
" if separator == -1:\n",
" separator = row['Measure Instrument'].find('-')\n",
" instrument.append(row['Measure Instrument'][:separator])\n",
" s = row['Measure Instrument'][separator+2:]\n",
" paren = s.find('(')\n",
" if paren > -1:\n",
" subtype.append(s[:paren-1])\n",
" units.append(s[paren+1:-1])\n",
" else:\n",
" subtype.append(s)\n",
" if s.endswith('scale'):\n",
" units.append('Scale')\n",
" else:\n",
" units.append('Score')\n",
" \n",
"new_cols = pd.DataFrame({'instrument': instrument, 'subtype': subtype, \n",
" 'units': units}, index=outcomes.index)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Eyberg Child Behaviour Inventory, Intensity Subscale 79\n",
"Eyberg Child Behaviour Inventory, Problem Subscale 57\n",
"Child Behavior Checklist, Externalizing (T Score) 46\n",
"Child Behavior Checklist, Externalizing 11\n",
"Strengths and Difficulties Questionnaire- Conduct Problems Scale 10\n",
"Strengths and Difficulties Questionnaire, Total Score 10\n",
"Eyberg Child Behaviour Inventory, Intensity Subscale (T Score) 10\n",
"Child Behavior Checklist, Conduct Problems (T Score) 6\n",
"Child Behavior Checklist, Aggression 6\n",
"Eyberg Child Behaviour Inventory, Problem Subscale (T Score) 4\n",
"Strengths and Difficulties Questionnaire- Emotional Symptoms Scale 4\n",
"Child Behavior Checklist, Delinquent Subscale 4\n",
"DSM IV 2\n",
"Childrens Global Assessment Scale 2\n",
"Strengths and Difficulties Questionnaire, Percent Below Clinical Cutoff 2\n",
"Strengths and Difficulties Questionnaire- Hyperactivity Scale 2\n",
"Child Behavior Checklist, Rulebreaking 2\n",
"Child Behavior Checklist, Conduct Problems 2\n",
"Child Behavior Checklist, Social problems 2\n",
"Strengths and Difficulties Questionnaire- Impact Score 2\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outcomes['Measure Instrument'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>instrument</th>\n",
" <th>subtype</th>\n",
" <th>units</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Intensity Subscale</td>\n",
" <td> T Score</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Intensity Subscale</td>\n",
" <td> T Score</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Problem Subscale</td>\n",
" <td> T Score</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Problem Subscale</td>\n",
" <td> T Score</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Child Behavior Checklist</td>\n",
" <td> Externalizing</td>\n",
" <td> Score</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instrument subtype units\n",
"0 Eyberg Child Behaviour Inventory Intensity Subscale T Score\n",
"1 Eyberg Child Behaviour Inventory Intensity Subscale T Score\n",
"2 Eyberg Child Behaviour Inventory Problem Subscale T Score\n",
"3 Eyberg Child Behaviour Inventory Problem Subscale T Score\n",
"4 Child Behavior Checklist Externalizing Score"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cols.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Append new columns\n",
"outcomes = outcomes.join(new_cols)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"wlc 47\n",
"tau 39\n",
"iypt 32\n",
"pcit 16\n",
"TAU 10\n",
"iyptndiyct 8\n",
"pppsd 7\n",
"WLC 7\n",
"mst 5\n",
"PCIT 5\n",
"MTP 5\n",
"modularndn 4\n",
"pppo 4\n",
"ppcp 4\n",
"it 4\n",
"iyct 4\n",
"CT 3\n",
"FBT 3\n",
"pmtndp 3\n",
"PLL 3\n",
"pppe 3\n",
"snap 3\n",
"spokes 3\n",
"pcitc 3\n",
"pmto 3\n",
"hncstd 2\n",
"projndsupport 2\n",
"TIK 2\n",
"pmtpa 2\n",
"pmtnds 2\n",
"pmtsd 2\n",
"setpc 2\n",
"hncte 2\n",
"RST 2\n",
"pcitabb 2\n",
"sst 1\n",
"pppstd 1\n",
"cbt 1\n",
"coaching 1\n",
"cpp 1\n",
"hnc 1\n",
"modularndclin 1\n",
"itpt 1\n",
"iyptadv 1\n",
"scip 1\n",
"modularndcomm 1\n",
"mcfi 1\n",
"kitkashrut 1\n",
"hitkashrut 1\n",
"pstnds 1\n",
"dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outcomes.intvn.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 23, 100, 103, 371, 441, 475, 539, 564, 757, 836, 870,\n",
" 899, 993, 995, 1018, 1096, 1201, 1215, 1236, 1245, 1292, 1386,\n",
" 1511, 1521, 1585, 1875, 1951, 2092, 2117, 2219, 2239, 2347, 3211,\n",
" 3225, 3397, 3399, 3495, 3687, 3716, 3766, 3915, 3960, 7109, 7723,\n",
" 8192, 8231, 8289, 8292, 8385, 8401, 7848])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outcomes['Ref ID'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data summaries\n",
"\n",
"Cross-tabulation of the outcome counts by measure instrument"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Outcome</th>\n",
" <th>01 Behavior, disruptive</th>\n",
" <th>02 Behavior, aggression</th>\n",
" <th>06 Behavior, fighting, destruction, violation</th>\n",
" <th>08 Behavior, other</th>\n",
" <th>11 Function, interpersonal and social</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Child Behavior Checklist</th>\n",
" <td> 65</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Childrens Global Assessment Scal</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSM I</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eyberg Child Behaviour Inventory</th>\n",
" <td> 148</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Strengths and Difficulties Questionnaire</th>\n",
" <td> 10</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Outcome 01 Behavior, disruptive \\\n",
"instrument \n",
"Child Behavior Checklist 65 \n",
"Childrens Global Assessment Scal 0 \n",
"DSM I 2 \n",
"Eyberg Child Behaviour Inventory 148 \n",
"Strengths and Difficulties Questionnaire 10 \n",
"\n",
"Outcome 02 Behavior, aggression \\\n",
"instrument \n",
"Child Behavior Checklist 4 \n",
"Childrens Global Assessment Scal 0 \n",
"DSM I 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 0 \n",
"\n",
"Outcome 06 Behavior, fighting, destruction, violation \\\n",
"instrument \n",
"Child Behavior Checklist 4 \n",
"Childrens Global Assessment Scal 0 \n",
"DSM I 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 0 \n",
"\n",
"Outcome 08 Behavior, other \\\n",
"instrument \n",
"Child Behavior Checklist 4 \n",
"Childrens Global Assessment Scal 0 \n",
"DSM I 0 \n",
"Eyberg Child Behaviour Inventory 2 \n",
"Strengths and Difficulties Questionnaire 20 \n",
"\n",
"Outcome 11 Function, interpersonal and social \n",
"instrument \n",
"Child Behavior Checklist 2 \n",
"Childrens Global Assessment Scal 2 \n",
"DSM I 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 0 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(outcomes['instrument'], outcomes['Outcome'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Distribution of age categories"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SCHOOL 44\n",
"PRE-K 31\n",
"TEEN 18\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"study_char.AgeCat.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Frequencies of various intervention types"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"PHARM 20\n",
"IY-PT 10\n",
"MST 9\n",
"PCIT 7\n",
"IY-PT + IY-CT 4\n",
"BSFT 3\n",
"Triple P (enhanced) 3\n",
"PMTO 3\n",
"OTH: Modular treatment 2\n",
"PT 2\n",
"IY-PT (nurse led) 2\n",
"CBT 2\n",
"PCIT-ABB 2\n",
"CPS 2\n",
"OTH: Intensive treatment 2\n",
"Triple-P (self-directed) 2\n",
"UCPP 2\n",
"SNAP Under 12 \\nOutReach Project(ORP) 1\n",
"IY-PT + ADVANCE 1\n",
"RST 1\n",
"OTH: Modular treatment (community) 1\n",
"PMT (practitioner assisted) 1\n",
"SCIP (Social cognitive (Dodge's)) 1\n",
"OTH: Project support 1\n",
"SET-PC 1\n",
"Triple-P (online) 1\n",
"Family Therapy 1\n",
"OTH: Child only treatment 1\n",
"MF-PEP + TAU 1\n",
"OTH: Family therapy 1\n",
"Coping Power (cultural adaptation) 1\n",
"PLL 1\n",
"TIK 1\n",
"Parenting Group (SPOKES) 1\n",
"SNAP Under 12 OutReach Project 1\n",
"PHARM1 + PHARM2 1\n",
"HNC (technology enhanced) 1\n",
"IY-PT + IY-CT + IY-TT 1\n",
"IY-PT (brief) 1\n",
"PONI 1\n",
"OTH: Modular (nurse administered) 1\n",
"IY 1\n",
"OTH: Instrumental, emotional support & child management skills 1\n",
"OTH: Community Parent Education Program 1\n",
"Triple-P (enhanced) 1\n",
"OTH: Parents Plus Children's Program 1\n",
"PMT (perceptive) 1\n",
"SNAP Under 12 OutReach Project (enhanced) 1\n",
"PCIT (modified) 1\n",
"OTH: FFT 1\n",
"OTH: MTP 1\n",
"IY-CT + IY-PT 1\n",
"HNC 1\n",
"CBFI 1\n",
"OTH: Booster 1\n",
"Length: 55, dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"study_char['Intervention Type'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract variables of interest and merge tables"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"KQ1 = study_char[study_char.KQ=='KQ1']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"study_varnames = ['Year', 'age_cat', 'Geographic setting', 'Age mean (years) ', 'Age SD (years)', \n",
" 'Age min (years)', 'Age max (years)', 'Proportion Male (%)', 'Design']\n",
"\n",
"study_vars = KQ1[study_varnames].rename(columns={'Geographic setting': 'country', \n",
" 'Age mean (years) ': 'age_mean', \n",
" 'Age SD (years)': 'age_sd', \n",
" 'Age min (years)': 'age_min', \n",
" 'Age max (years)': 'age_max', \n",
" 'Proportion Male (%)': 'p_male'})"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>age_cat</th>\n",
" <th>country</th>\n",
" <th>age_mean</th>\n",
" <th>age_sd</th>\n",
" <th>age_min</th>\n",
" <th>age_max</th>\n",
" <th>p_male</th>\n",
" <th>Design</th>\n",
" </tr>\n",
" <tr>\n",
" <th>RefID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 2013</td>\n",
" <td> 1</td>\n",
" <td> USA</td>\n",
" <td> 2.80</td>\n",
" <td> 0.61</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 62</td>\n",
" <td> RCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> 2013</td>\n",
" <td> 2</td>\n",
" <td> USA</td>\n",
" <td> 14.60</td>\n",
" <td> 1.30</td>\n",
" <td> 11</td>\n",
" <td> 18</td>\n",
" <td> 83</td>\n",
" <td> RCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> 2013</td>\n",
" <td> 1</td>\n",
" <td> USA</td>\n",
" <td> 5.67</td>\n",
" <td> 1.72</td>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td> 53</td>\n",
" <td> RCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td> 2013</td>\n",
" <td> 0</td>\n",
" <td> USA</td>\n",
" <td> 9.90</td>\n",
" <td> 1.30</td>\n",
" <td> 8</td>\n",
" <td> 11</td>\n",
" <td> 73</td>\n",
" <td> RCT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td> 2013</td>\n",
" <td> 2</td>\n",
" <td> Netherlands</td>\n",
" <td> 16.00</td>\n",
" <td> 1.31</td>\n",
" <td> 12</td>\n",
" <td> 18</td>\n",
" <td> 73</td>\n",
" <td> RCT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year age_cat country age_mean age_sd age_min age_max p_male \\\n",
"RefID \n",
"23 2013 1 USA 2.80 0.61 2 4 62 \n",
"100 2013 2 USA 14.60 1.30 11 18 83 \n",
"103 2013 1 USA 5.67 1.72 3 8 53 \n",
"141 2013 0 USA 9.90 1.30 8 11 73 \n",
"156 2013 2 Netherlands 16.00 1.31 12 18 73 \n",
"\n",
" Design \n",
"RefID \n",
"23 RCT \n",
"100 RCT \n",
"103 RCT \n",
"141 RCT \n",
"156 RCT "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"study_vars.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115708208>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1156e4898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"study_vars.p_male.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proportion missing"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Year 0.00\n",
"age_cat 0.00\n",
"country 0.00\n",
"age_mean 0.10\n",
"age_sd 0.19\n",
"age_min 0.06\n",
"age_max 0.07\n",
"p_male 0.02\n",
"Design 0.00\n",
"dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"study_vars.isnull().mean(0).round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Will assume the mean age for those which are missing is simply the midpoint between minimum and maximum values"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"est_means = study_vars.apply(lambda x: x.age_min + (x.age_max - x.age_min) / 2, axis=1)[study_vars.age_mean.isnull()]\n",
"study_vars.loc[study_vars.age_mean.isnull(), 'age_mean'] = est_means\n",
"\n",
"study_vars.age_mean.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"outcomes_varnames = ['Ref ID', 'Measure Instrument', 'instrument', 'subtype', 'units', \n",
" 'intvn', 'cc', 'pc', 'fc',\n",
" 'BL N', 'BL Mean', 'BL SD', \n",
" 'EOT \\nN', 'EOT Mean', 'EOT \\nSD', 'Last FU Time', 'Last FU N', \n",
" 'Last FU Mean', 'Last FU SD', 'CS Group N', 'CS Mean', 'CS SD']"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"outcomes_vars = outcomes[outcomes_varnames].rename(columns={'Ref ID': 'RefID', \n",
" 'Measure Instrument': 'measure_instrument',\n",
" 'cc': 'child_component',\n",
" 'pc': 'parent_component',\n",
" 'fc': 'family_component',\n",
" 'oc': 'other_component',\n",
" 'BL N': 'baseline_n',\n",
" 'BL Mean': 'baseline_mean',\n",
" 'BL SD': 'baseline_sd', \n",
" 'EOT \\nN': 'end_treat_n', \n",
" 'EOT Mean': 'end_treat_mean', \n",
" 'EOT \\nSD': 'end_treat_sd', \n",
" 'Last FU Time': 'followup_time', \n",
" 'Last FU N': 'followup_n',\n",
" 'Last FU Mean': 'followup_mean', \n",
" 'Last FU SD': 'followup_sd', \n",
" 'CS Group N': 'change_n',\n",
" 'CS Mean': 'change_mean',\n",
" 'CS SD': 'change_sd'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode intervention clasification"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RefID 0\n",
"measure_instrument 0\n",
"instrument 0\n",
"subtype 0\n",
"units 0\n",
"intvn 0\n",
"child_component 0\n",
"parent_component 0\n",
"family_component 0\n",
"baseline_n 4\n",
"baseline_mean 17\n",
"baseline_sd 25\n",
"end_treat_n 74\n",
"end_treat_mean 67\n",
"end_treat_sd 74\n",
"followup_time 32\n",
"followup_n 103\n",
"followup_mean 118\n",
"followup_sd 126\n",
"change_n 251\n",
"change_mean 258\n",
"change_sd 263\n",
"dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"outcomes_vars.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>child_component</th>\n",
" <th>parent_component</th>\n",
" <th>family_component</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>238</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" child_component parent_component family_component\n",
"125 1 0 0\n",
"126 1 0 0\n",
"128 1 0 0\n",
"129 1 0 0\n",
"162 1 0 0\n",
"188 1 0 0\n",
"194 1 0 0\n",
"195 1 0 0\n",
"234 1 0 0\n",
"236 1 0 0\n",
"238 1 0 0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"control = ((outcomes_vars.child_component^True) & \n",
" (outcomes_vars.parent_component^True) & \n",
" (outcomes_vars.family_component^True)).astype(int)\n",
"child_only = ((outcomes_vars.child_component) & \n",
" (outcomes_vars.parent_component^True) & \n",
" (outcomes_vars.family_component^True)).astype(int)\n",
"parent_only = ((outcomes_vars.child_component^True) & \n",
" (outcomes_vars.parent_component) & \n",
" (outcomes_vars.family_component^True)).astype(int)\n",
"outcomes_vars.ix[child_only.astype(bool), ['child_component', 'parent_component', 'family_component']]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"multi_component = ((parent_only^True) & (child_only^True) & (control^True)).astype(int)\n",
"\n",
"outcomes_vars['child_only'] = child_only\n",
"outcomes_vars['parent_only'] = parent_only\n",
"outcomes_vars['multi_component'] = multi_component"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If requested, change PCIT to parent-only."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pcit_as_multicomponent = True\n",
"\n",
"if not pcit_as_multicomponent:\n",
" outcomes_vars.loc[outcomes_vars.intvn=='pcit', 'parenr_only'] = 1\n",
" outcomes_vars.loc[outcomes_vars.intvn=='pcit', 'multi_component'] = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obtain subset with non-missing EOT data"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eot_subset = outcomes_vars[outcomes_vars.end_treat_mean.notnull() & outcomes_vars.end_treat_sd.notnull()].copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate EOT difference"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eot_subset['eot_diff_mean'] = eot_subset.end_treat_mean - eot_subset.baseline_mean"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eot_subset['eot_diff_sd'] = eot_subset.baseline_sd + eot_subset.end_treat_sd"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"eot_subset['eot_diff_n'] = eot_subset[['baseline_n', 'end_treat_n']].min(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Distribution of baseline means among outcome metrics"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1158420f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1157779e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"s1JqWTLPDxldQUGSdz0RsRS4vWpUYd4tHxF/AQ4e7jgaOJD0nv4HWTMZnTCsUa32atJ9/GXAaRHx\n",
"IwBJ1wP7DGdg2StI/RosB26MiIXALgWKz8xKpJU1cxsASbOB0fWmRcTkIQ6nXgx/BP7Imi0ZuwxT\n",
"OPWcRmp1aQculzQ6Ii4h1dKL4DTSxcYIihmfmZWIk3lxnQJ8HziE9NO+Qin6xQawLCIWwKqfqF0v\n",
"6e/AsP8sLVsWEU9BYeMzsxJxMi+oiLhN0v8A20ZEEW9VFPpiA/g/SWcCUyNicX4w71pg/DDHVVH0\n",
"+MysRJzMCywiTh/uGHpSgouNDwBHkmu6EfGIpL2BU4c1qtWKHp+ZlYj7MzczMyu5orwgxczMzPrJ\n",
"ydzMzKzknMzNzMxKzsnczMys5JzMzczMSu7/AxjD5nkpWSB/AAAAAElFTkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x115a44908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for instrument in ('Eyberg Child Behaviour Inventory', \n",
" 'Child Behavior Checklist', \n",
" 'Strengths and Difficulties Questionnaire'):\n",
" eot_subset[eot_subset.instrument==instrument]['baseline_mean'].hist(by=eot_subset['subtype'], \n",
" sharex=True)\n",
" plt.suptitle(instrument);"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Eyberg Child Behaviour Inventory 116\n",
"Child Behavior Checklist 48\n",
"Strengths and Difficulties Questionnaire 22\n",
"Childrens Global Assessment Scal 2\n",
"dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eot_subset.instrument.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Eyberg Child Behaviour Inventory 116\n",
"Child Behavior Checklist 48\n",
"Strengths and Difficulties Questionnaire 22\n",
"Childrens Global Assessment Scal 2\n",
"dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eot_subset.instrument.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RefID</th>\n",
" <th>measure_instrument</th>\n",
" <th>instrument</th>\n",
" <th>subtype</th>\n",
" <th>units</th>\n",
" <th>intvn</th>\n",
" <th>child_component</th>\n",
" <th>parent_component</th>\n",
" <th>family_component</th>\n",
" <th>baseline_n</th>\n",
" <th>...</th>\n",
" <th>followup_sd</th>\n",
" <th>change_n</th>\n",
" <th>change_mean</th>\n",
" <th>change_sd</th>\n",
" <th>child_only</th>\n",
" <th>parent_only</th>\n",
" <th>multi_component</th>\n",
" <th>eot_diff_mean</th>\n",
" <th>eot_diff_sd</th>\n",
" <th>eot_diff_n</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 441</td>\n",
" <td> Eyberg Child Behaviour Inventory, Intensity Su...</td>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Intensity Subscale</td>\n",
" <td> Scale</td>\n",
" <td> iypt</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 32</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td>-31.40</td>\n",
" <td> 46.80</td>\n",
" <td> 32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 441</td>\n",
" <td> Eyberg Child Behaviour Inventory, Intensity Su...</td>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Intensity Subscale</td>\n",
" <td> Scale</td>\n",
" <td> wlc</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> -5.80</td>\n",
" <td> 49.60</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 441</td>\n",
" <td> Eyberg Child Behaviour Inventory, Problem Subs...</td>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Problem Subscale</td>\n",
" <td> Scale</td>\n",
" <td> iypt</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 24</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> -9.70</td>\n",
" <td> 12.02</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 441</td>\n",
" <td> Eyberg Child Behaviour Inventory, Problem Subs...</td>\n",
" <td> Eyberg Child Behaviour Inventory</td>\n",
" <td> Problem Subscale</td>\n",
" <td> Scale</td>\n",
" <td> wlc</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 17</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> -2.88</td>\n",
" <td> 14.59</td>\n",
" <td> 17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" RefID measure_instrument \\\n",
"14 441 Eyberg Child Behaviour Inventory, Intensity Su... \n",
"15 441 Eyberg Child Behaviour Inventory, Intensity Su... \n",
"16 441 Eyberg Child Behaviour Inventory, Problem Subs... \n",
"17 441 Eyberg Child Behaviour Inventory, Problem Subs... \n",
"\n",
" instrument subtype units intvn \\\n",
"14 Eyberg Child Behaviour Inventory Intensity Subscale Scale iypt \n",
"15 Eyberg Child Behaviour Inventory Intensity Subscale Scale wlc \n",
"16 Eyberg Child Behaviour Inventory Problem Subscale Scale iypt \n",
"17 Eyberg Child Behaviour Inventory Problem Subscale Scale wlc \n",
"\n",
" child_component parent_component family_component baseline_n \\\n",
"14 0 1 0 32 \n",
"15 0 0 0 20 \n",
"16 0 1 0 24 \n",
"17 0 0 0 17 \n",
"\n",
" ... followup_sd change_n change_mean change_sd child_only \\\n",
"14 ... NaN NaN NaN NaN 0 \n",
"15 ... NaN NaN NaN NaN 0 \n",
"16 ... NaN NaN NaN NaN 0 \n",
"17 ... NaN NaN NaN NaN 0 \n",
"\n",
" parent_only multi_component eot_diff_mean eot_diff_sd eot_diff_n \n",
"14 1 0 -31.40 46.80 32 \n",
"15 0 0 -5.80 49.60 20 \n",
"16 1 0 -9.70 12.02 24 \n",
"17 0 0 -2.88 14.59 17 \n",
"\n",
"[4 rows x 28 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eot_subset[eot_subset.RefID==441]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several studies use multiple instruments and metrics within instruments"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RefID instrument \n",
"103 Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"371 Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"441 Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"475 Eyberg Child Behaviour Inventory Intensity Subscale 2\n",
"539 Child Behavior Checklist Aggression 2\n",
" Externalizing 2\n",
"564 Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"836 Strengths and Difficulties Questionnaire Total Score 2\n",
"899 Child Behavior Checklist Externalizing 3\n",
" Eyberg Child Behaviour Inventory Problem Subscale 3\n",
" Intensity Subscale 3\n",
"...\n",
"7723 Child Behavior Checklist Externalizing 2\n",
"7848 Child Behavior Checklist Aggression 2\n",
" Externalizing 2\n",
" Delinquent Subscale 2\n",
" Social problems 2\n",
" Childrens Global Assessment Scal hildrens Global Assessment Scale 2\n",
"8192 Eyberg Child Behaviour Inventory Intensity Subscale 2\n",
"8231 Eyberg Child Behaviour Inventory Problem Subscale 4\n",
" Intensity Subscale 4\n",
"8289 Child Behavior Checklist Delinquent Subscale 2\n",
" Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"8292 Child Behavior Checklist Externalizing 2\n",
"8401 Eyberg Child Behaviour Inventory Problem Subscale 2\n",
" Intensity Subscale 2\n",
"Length: 73, dtype: int64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eot_subset.groupby(['RefID', 'instrument'])['subtype'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>subtype</th>\n",
" <th>Aggression</th>\n",
" <th>Conduct Problems</th>\n",
" <th>Conduct Problems Scale</th>\n",
" <th>Delinquent Subscale</th>\n",
" <th>Emotional Symptoms Scale</th>\n",
" <th>Externalizing</th>\n",
" <th>Hyperactivity Scale</th>\n",
" <th>Impact Score</th>\n",
" <th>Intensity Subscale</th>\n",
" <th>Problem Subscale</th>\n",
" <th>Social problems</th>\n",
" <th>Total Score</th>\n",
" <th>hildrens Global Assessment Scale</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Child Behavior Checklist</th>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 32</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Childrens Global Assessment Scal</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eyberg Child Behaviour Inventory</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 68</td>\n",
" <td> 48</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Strengths and Difficulties Questionnaire</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"subtype Aggression Conduct Problems \\\n",
"instrument \n",
"Child Behavior Checklist 4 6 \n",
"Childrens Global Assessment Scal 0 0 \n",
"Eyberg Child Behaviour Inventory 0 0 \n",
"Strengths and Difficulties Questionnaire 0 0 \n",
"\n",
"subtype Conduct Problems Scale \\\n",
"instrument \n",
"Child Behavior Checklist 0 \n",
"Childrens Global Assessment Scal 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 6 \n",
"\n",
"subtype Delinquent Subscale \\\n",
"instrument \n",
"Child Behavior Checklist 4 \n",
"Childrens Global Assessment Scal 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 0 \n",
"\n",
"subtype Emotional Symptoms Scale \\\n",
"instrument \n",
"Child Behavior Checklist 0 \n",
"Childrens Global Assessment Scal 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 4 \n",
"\n",
"subtype Externalizing Hyperactivity Scale \\\n",
"instrument \n",
"Child Behavior Checklist 32 0 \n",
"Childrens Global Assessment Scal 0 0 \n",
"Eyberg Child Behaviour Inventory 0 0 \n",
"Strengths and Difficulties Questionnaire 0 2 \n",
"\n",
"subtype Impact Score Intensity Subscale \\\n",
"instrument \n",
"Child Behavior Checklist 0 0 \n",
"Childrens Global Assessment Scal 0 0 \n",
"Eyberg Child Behaviour Inventory 0 68 \n",
"Strengths and Difficulties Questionnaire 2 0 \n",
"\n",
"subtype Problem Subscale Social problems \\\n",
"instrument \n",
"Child Behavior Checklist 0 2 \n",
"Childrens Global Assessment Scal 0 0 \n",
"Eyberg Child Behaviour Inventory 48 0 \n",
"Strengths and Difficulties Questionnaire 0 0 \n",
"\n",
"subtype Total Score \\\n",
"instrument \n",
"Child Behavior Checklist 0 \n",
"Childrens Global Assessment Scal 0 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 8 \n",
"\n",
"subtype hildrens Global Assessment Scale \n",
"instrument \n",
"Child Behavior Checklist 0 \n",
"Childrens Global Assessment Scal 2 \n",
"Eyberg Child Behaviour Inventory 0 \n",
"Strengths and Difficulties Questionnaire 0 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(eot_subset.instrument, eot_subset.subtype)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>subtype</th>\n",
" <th>Intensity Subscale</th>\n",
" <th>Problem Subscale</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Eyberg Child Behaviour Inventory</th>\n",
" <td> 68</td>\n",
" <td> 48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"subtype Intensity Subscale Problem Subscale\n",
"instrument \n",
"Eyberg Child Behaviour Inventory 68 48"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = eot_subset[eot_subset.instrument=='Eyberg Child Behaviour Inventory']\n",
"pd.crosstab(x.instrument, x.subtype)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>subtype</th>\n",
" <th>Aggression</th>\n",
" <th>Conduct Problems</th>\n",
" <th>Delinquent Subscale</th>\n",
" <th>Externalizing</th>\n",
" <th>Social problems</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Child Behavior Checklist</th>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 4</td>\n",
" <td> 32</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"subtype Aggression Conduct Problems Delinquent Subscale \\\n",
"instrument \n",
"Child Behavior Checklist 4 6 4 \n",
"\n",
"subtype Externalizing Social problems \n",
"instrument \n",
"Child Behavior Checklist 32 2 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = eot_subset[eot_subset.instrument=='Child Behavior Checklist']\n",
"pd.crosstab(x.instrument, x.subtype)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>subtype</th>\n",
" <th>Conduct Problems Scale</th>\n",
" <th>Emotional Symptoms Scale</th>\n",
" <th>Hyperactivity Scale</th>\n",
" <th>Impact Score</th>\n",
" <th>Total Score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>instrument</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Strengths and Difficulties Questionnaire</th>\n",
" <td> 6</td>\n",
" <td> 4</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"subtype Conduct Problems Scale \\\n",
"instrument \n",
"Strengths and Difficulties Questionnaire 6 \n",
"\n",
"subtype Emotional Symptoms Scale \\\n",
"instrument \n",
"Strengths and Difficulties Questionnaire 4 \n",
"\n",
"subtype Hyperactivity Scale Impact Score \\\n",
"instrument \n",
"Strengths and Difficulties Questionnaire 2 2 \n",
"\n",
"subtype Total Score \n",
"instrument \n",
"Strengths and Difficulties Questionnaire 8 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = eot_subset[eot_subset.instrument=='Strengths and Difficulties Questionnaire']\n",
"pd.crosstab(x.instrument, x.subtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter out non-RCT studies, if requested."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rct_only = True\n",
"\n",
"rct_study_vars = study_vars[study_vars.Design=='RCT']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Merge study variables and outcomes"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(156, 37)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_vars = rct_study_vars.merge(eot_subset, left_index=True, right_on='RefID')\n",
"merged_vars.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For now, restrict to the three most prevalent metrics."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Eyberg Child Behaviour Inventory, Intensity Subscale 62\n",
"Eyberg Child Behaviour Inventory, Problem Subscale 46\n",
"Child Behavior Checklist, Externalizing (T Score) 19\n",
"Child Behavior Checklist, Externalizing 7\n",
"Child Behavior Checklist, Conduct Problems (T Score) 6\n",
"Eyberg Child Behaviour Inventory, Intensity Subscale (T Score) 4\n",
"Strengths and Difficulties Questionnaire, Total Score 4\n",
"Strengths and Difficulties Questionnaire- Emotional Symptoms Scale 2\n",
"Child Behavior Checklist, Aggression 2\n",
"Child Behavior Checklist, Delinquent Subscale 2\n",
"Strengths and Difficulties Questionnaire- Conduct Problems Scale 2\n",
"dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_vars.measure_instrument.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take only the top 3 measures"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"top_measures = 3"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 103, 371, 441, 475, 539, 836, 899, 1236, 1245, 1511, 1585,\n",
" 1875, 1951, 2092, 2219, 2239, 3211, 3225, 3399, 3495, 3687, 3716,\n",
" 3766, 3915, 3960, 7109, 7723, 8192, 8231, 8289, 8292, 8401])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_vars.RefID.unique()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"measure_instrument\n",
"Child Behavior Checklist, Externalizing (T Score) 77.10\n",
"Eyberg Child Behaviour Inventory, Intensity Subscale 186.44\n",
"Eyberg Child Behaviour Inventory, Problem Subscale 28.62\n",
"Name: baseline_mean, dtype: float64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset = merged_vars[merged_vars.measure_instrument.isin(merged_vars.measure_instrument.value_counts().index[:top_measures])]\n",
"analysis_subset.groupby('measure_instrument')['baseline_mean'].max()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"measure_instrument\n",
"Child Behavior Checklist, Externalizing (T Score) Axes(0.125,0.125;0.775x0.775)\n",
"Eyberg Child Behaviour Inventory, Intensity Subscale Axes(0.125,0.125;0.775x0.775)\n",
"Eyberg Child Behaviour Inventory, Problem Subscale Axes(0.125,0.125;0.775x0.775)\n",
"Name: baseline_mean, dtype: object"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x116121f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis_subset.groupby('measure_instrument').baseline_mean.hist()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1160ede10>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x1162e2ba8>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x1163d47f0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x115ecd748>]], dtype=object)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11628b9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis_subset['baseline_mean'].hist(by=analysis_subset['measure_instrument'],sharex=True)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x115e46780>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x115b72f28>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x115ef85f8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x115b2e470>]], dtype=object)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1161219e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis_subset['eot_diff_mean'].hist(by=analysis_subset['measure_instrument'],sharex=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115ec6ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x116002f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115df8e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for x in analysis_subset.measure_instrument.unique():\n",
" plt.figure()\n",
" analysis_subset[analysis_subset.measure_instrument==x].baseline_mean.plot(kind='kde', alpha=0.4, grid=False)\n",
" analysis_subset[analysis_subset.measure_instrument==x].end_treat_mean.plot(kind='kde', alpha=0.4, grid=False)\n",
" plt.gca().set_xlabel(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of TAU/Control arms in subset"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"49"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.shape[0] - analysis_subset.multi_component.sum() - analysis_subset.child_only.sum() - analysis_subset.parent_only.sum()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{539, 899, 1236, 1511, 2092, 3399, 3687, 3766, 3915, 8192, 8231, 8289, 8292}"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(analysis_subset[analysis_subset.multi_component==1].RefID.values)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1875, 3766, 8289}"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(analysis_subset[analysis_subset.child_only==1].RefID.values)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{103,\n",
" 371,\n",
" 441,\n",
" 1236,\n",
" 1245,\n",
" 1585,\n",
" 2219,\n",
" 3211,\n",
" 3225,\n",
" 3495,\n",
" 3716,\n",
" 3766,\n",
" 3960,\n",
" 7109,\n",
" 7723,\n",
" 8401}"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(analysis_subset[analysis_subset.parent_only==1].RefID.values)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{371,\n",
" 441,\n",
" 539,\n",
" 899,\n",
" 1236,\n",
" 1511,\n",
" 1585,\n",
" 1875,\n",
" 2092,\n",
" 2219,\n",
" 3211,\n",
" 3225,\n",
" 3399,\n",
" 3495,\n",
" 3687,\n",
" 3716,\n",
" 3766,\n",
" 3915,\n",
" 7109,\n",
" 7723,\n",
" 8192,\n",
" 8231,\n",
" 8292,\n",
" 8401}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(analysis_subset[(analysis_subset.parent_only==0) &\n",
" (analysis_subset.child_only==0) &\n",
" (analysis_subset.multi_component==0)].RefID.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Total arm sample sizes"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"190.0"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.loc[analysis_subset.child_only==1, 'baseline_n'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1540.0"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.loc[analysis_subset.parent_only==1, 'baseline_n'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"929.0"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.loc[analysis_subset.multi_component==1, 'baseline_n'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1348.0"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.loc[(analysis_subset.multi_component==0) & (analysis_subset.parent_only==0) & (analysis_subset.child_only==0), 'baseline_n'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Meta-analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of studies in analysis subset"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"28"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_studies = analysis_subset.RefID.unique().tolist()\n",
"len(unique_studies)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 103, 371, 441, 539, 899, 1236, 1245, 1511, 1585, 1875, 2092,\n",
" 2219, 3211, 3225, 3399, 3495, 3687, 3716, 3766, 3915, 3960, 7109,\n",
" 7723, 8192, 8231, 8289, 8292, 8401])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.RefID.unique()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>measure_instrument</th>\n",
" <th>Child Behavior Checklist, Externalizing (T Score)</th>\n",
" <th>Eyberg Child Behaviour Inventory, Intensity Subscale</th>\n",
" <th>Eyberg Child Behaviour Inventory, Problem Subscale</th>\n",
" </tr>\n",
" <tr>\n",
" <th>RefID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>103 </th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371 </th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441 </th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>539 </th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>899 </th>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1236</th>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1245</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1511</th>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1585</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1875</th>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2092</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2219</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3211</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3225</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3399</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3495</th>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3687</th>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3716</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3766</th>\n",
" <td> 0</td>\n",
" <td> 9</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3915</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3960</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7109</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7723</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8192</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8231</th>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8289</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8292</th>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8401</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"measure_instrument Child Behavior Checklist, Externalizing (T Score) \\\n",
"RefID \n",
"103 0 \n",
"371 0 \n",
"441 0 \n",
"539 2 \n",
"899 3 \n",
"1236 0 \n",
"1245 2 \n",
"1511 3 \n",
"1585 0 \n",
"1875 3 \n",
"2092 0 \n",
"2219 0 \n",
"3211 0 \n",
"3225 0 \n",
"3399 2 \n",
"3495 0 \n",
"3687 0 \n",
"3716 0 \n",
"3766 0 \n",
"3915 0 \n",
"3960 0 \n",
"7109 0 \n",
"7723 2 \n",
"8192 0 \n",
"8231 0 \n",
"8289 0 \n",
"8292 2 \n",
"8401 0 \n",
"\n",
"measure_instrument Eyberg Child Behaviour Inventory, Intensity Subscale \\\n",
"RefID \n",
"103 2 \n",
"371 2 \n",
"441 2 \n",
"539 0 \n",
"899 3 \n",
"1236 6 \n",
"1245 0 \n",
"1511 0 \n",
"1585 2 \n",
"1875 0 \n",
"2092 3 \n",
"2219 3 \n",
"3211 2 \n",
"3225 2 \n",
"3399 0 \n",
"3495 4 \n",
"3687 6 \n",
"3716 2 \n",
"3766 9 \n",
"3915 2 \n",
"3960 0 \n",
"7109 2 \n",
"7723 0 \n",
"8192 2 \n",
"8231 4 \n",
"8289 2 \n",
"8292 0 \n",
"8401 2 \n",
"\n",
"measure_instrument Eyberg Child Behaviour Inventory, Problem Subscale \n",
"RefID \n",
"103 2 \n",
"371 2 \n",
"441 2 \n",
"539 0 \n",
"899 3 \n",
"1236 6 \n",
"1245 0 \n",
"1511 0 \n",
"1585 2 \n",
"1875 0 \n",
"2092 0 \n",
"2219 3 \n",
"3211 2 \n",
"3225 2 \n",
"3399 0 \n",
"3495 0 \n",
"3687 6 \n",
"3716 2 \n",
"3766 0 \n",
"3915 2 \n",
"3960 2 \n",
"7109 2 \n",
"7723 0 \n",
"8192 0 \n",
"8231 4 \n",
"8289 2 \n",
"8292 0 \n",
"8401 2 "
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis_subset.groupby(['RefID', 'measure_instrument'])['measure_instrument'].count().unstack().fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Study counts by "
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"child_component 9\n",
"parent_component 21\n",
"multi_component 8\n",
"dtype: int64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_by_refid = analysis_subset.groupby('RefID')\n",
"data_by_refid.apply(lambda x: (x[['child_component', 'parent_component', 'multi_component']].sum()>1)).sum()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RefID\n",
"103 0\n",
"371 1\n",
"441 1\n",
"539 1\n",
"899 1\n",
"1236 1\n",
"1245 0\n",
"1511 1\n",
"1585 1\n",
"1875 1\n",
"2092 1\n",
"2219 1\n",
"3211 1\n",
"3225 1\n",
"3399 1\n",
"3495 1\n",
"3687 1\n",
"3716 1\n",
"3766 1\n",
"3915 1\n",
"3960 0\n",
"7109 1\n",
"7723 1\n",
"8192 1\n",
"8231 1\n",
"8289 0\n",
"8292 1\n",
"8401 1\n",
"dtype: int64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_by_refid.apply(lambda x: (x[['child_component', 'parent_component', 'multi_component']].sum(1)==0).sum()>0).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 16\n",
"0 10\n",
"2 2\n",
"dtype: int64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_by_refid.apply(lambda x: x.age_cat.unique()[0]).value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are restricting the analysis to the 4 most prevalent measure instruments in the database."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Eyberg Child Behaviour Inventory, Intensity Subscale',\n",
" 'Eyberg Child Behaviour Inventory, Problem Subscale',\n",
" 'Child Behavior Checklist, Externalizing (T Score)']"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_measures = analysis_subset.measure_instrument.unique().tolist()\n",
"k = len(unique_measures)\n",
"unique_measures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Three intervention components were coded:\n",
"\n",
"* `child_component`\n",
"* `parent_component`\n",
"* `multi_component`"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p_male, age_cat, intvn = analysis_subset[['p_male', 'age_cat', 'intvn']].values.T\n",
"child_only, parent_only, multi_component = analysis_subset[['child_only', 'parent_only', \n",
" 'multi_component']].values.T\n",
"\n",
"change_n, change_mean, change_sd = analysis_subset[['eot_diff_n', 'eot_diff_mean', 'eot_diff_sd']].values.T"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"school = (analysis_subset.age_cat.values==0).astype(int)\n",
"pre_k = (analysis_subset.age_cat.values==1).astype(int)\n",
"teen = (analysis_subset.age_cat.values==2).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The response variable is a multivariate normal of dimension k=3, for each of the measure instruments:\n",
"\n",
"1. Eyberg Child Behaviour Inventory, Intensity Subscale\n",
"2. Eyberg Child Behaviour Inventory, Problem Subscale\n",
"3. Child Behavior Checklist, Externalizing (T Score)\n",
"\n",
"$$\\left({\n",
"\\begin{array}{c}\n",
" {m_1}\\\\\n",
" {m_2}\\\\\n",
" {m_3}\n",
"\\end{array}\n",
"}\\right)_i \\sim\\text{MVN}(\\mathbf{\\mu},\\Sigma)$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Means for each study are a draw from a multivariate normal."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wishart = False\n",
"\n",
"mu = pm.Normal('mu', 0, 0.001, value=[0]*k)\n",
"\n",
"if wishart:\n",
" T = pm.Wishart('T', k+1, np.eye(k), value=np.eye(k))\n",
" \n",
" m = [pm.MvNormal('m_{}'.format(i), mu, T, value=[0]*k) for i in range(len(unique_studies))]\n",
"else:\n",
" sigmas = pm.Uniform('sigmas', 0, 100, value=[10]*k)\n",
" rhos = pm.Uniform('rhos', -1, 1, value=[0]*int(comb(k, 2)))\n",
"\n",
" Sigma = pm.Lambda('Sigma', lambda s=sigmas, r=rhos: np.array([[s[0]**2, s[0]*s[1]*r[0], s[0]*s[2]*r[1]],\n",
" [s[0]*s[1]*r[0], s[1]**2, s[1]*s[2]*r[2]],\n",
" [s[0]*s[2]*r[1], s[1]*s[2]*r[2], s[2]**2]]))\n",
" \n",
" m = [pm.MvNormalCov('m_{}'.format(i), mu, Sigma, value=[0]*k) for i in range(len(unique_studies))]\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unique intervention labels for each component; we will use these for component random effects."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_child_intvn = np.unique(intvn[child_only.astype(bool)]).tolist()\n",
"unique_parent_intvn = np.unique(intvn[parent_only.astype(bool)]).tolist()\n",
"unique_multi_intvn = np.unique(intvn[multi_component.astype(bool)]).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Indices to random effect labels\n",
"child_component_index = [unique_child_intvn.index(x) for x in intvn[child_only.astype(bool)]]\n",
"parent_component_index = [unique_parent_intvn.index(x) for x in intvn[parent_only.astype(bool)]]\n",
"multi_component_index = [unique_multi_intvn.index(x) for x in intvn[multi_component.astype(bool)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Treatment component random effects\n",
"\n",
"$$X_i = \\left[{\n",
"\\begin{array}{c}\n",
" {x_c}\\\\\n",
" {x_p}\\\\\n",
" {x_m}\\\\\n",
"\\end{array}\n",
"}\\right]_i$$\n",
"\n",
"$$\\begin{aligned}\n",
"\\beta_j^{(c)} &\\sim N(\\mu_{\\beta}^{(c)},\\tau_{\\beta}^{(c)}) \\\\\n",
"\\beta_j^{(p)} &\\sim N(\\mu_{\\beta}^{(p)},\\tau_{\\beta}^{(p)}) \\\\\n",
"\\beta_j^{(m)} &\\sim N(\\mu_{\\beta}^{(m)},\\tau_{\\beta}^{(m)})\n",
"\\end{aligned}$$"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mu_beta = pm.Normal('mu_beta', 0, 0.001, value=[0]*3)\n",
"# sig_beta = pm.Uniform('sig_beta', 0, 100, value=1)\n",
"# tau_beta = sig_beta ** -2\n",
"tau_beta = pm.Gamma('tau_beta', 1, 0.1, value=1)\n",
"\n",
"beta_c = pm.Normal('beta_c', mu_beta[0], tau_beta, value=[0]*len(unique_child_intvn))\n",
"beta_p = pm.Normal('beta_p', mu_beta[1], tau_beta, value=[0]*len(unique_parent_intvn))\n",
"beta_m = pm.Normal('beta_m', mu_beta[2], tau_beta, value=[0]*len(unique_multi_intvn))\n",
"\n",
"b_c = pm.Lambda('b_c', lambda b=beta_c: \n",
" np.array([b[unique_child_intvn.index(x)] if child_only[i] else 0 for i,x in enumerate(intvn)]))\n",
"b_p = pm.Lambda('b_p', lambda b=beta_p: \n",
" np.array([b[unique_parent_intvn.index(x)] if parent_only[i] else 0 for i,x in enumerate(intvn)]))\n",
"b_m = pm.Lambda('b_m', lambda b=beta_m: \n",
" np.array([b[unique_multi_intvn.index(x)] if multi_component[i] else 0 for i,x in enumerate(intvn)]))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the probability of being the best intervention."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"best = pm.Lambda('best', lambda b=mu_beta: (b==b.min()).astype(int))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interaction of parent and multi-component with pre-k children."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"interaction = False\n",
"\n",
"if interaction:\n",
" beta_pk_p = pm.Normal('beta_pk_p', 0, 1e-5, value=0)\n",
" beta_pk_m = pm.Normal('beta_pk_m', 0, 1e-5, value=0)\n",
" b_pk_p = pm.Lambda('b_pk_p', lambda b=beta_pk_p: b * parent_only * pre_k)\n",
" b_pk_m = pm.Lambda('b_pk_m', lambda b=beta_pk_m: b * multi_component * pre_k)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"betas = b_c + b_p + b_m \n",
"\n",
"if interaction:\n",
" betas = betas + b_pk_p + b_pk_m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Covariate effects of age and percent female.\n",
"\n",
"$$\\alpha \\sim N(0, 1e5)$$"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"alpha_age = pm.Normal('alpha_age', 0, 1e-5, value=[1,2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unique study ID (`RefID`) and measure ID (`measure_instrument`) values."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"study_id = [unique_studies.index(x) for x in analysis_subset.RefID]\n",
"measure_id = [unique_measures.index(x) for x in analysis_subset.measure_instrument]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate expected response (treatment difference) as a function of treatment and covariates.\n",
"\n",
"$$\\theta_i = m_{j[i]k} + X_i \\beta + \\alpha x_{age}$$ "
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"baseline_sd = analysis_subset.baseline_sd.values\n",
"\n",
"@pm.deterministic\n",
"def theta(m=m, betas=betas, alpha_age=alpha_age): \n",
"\n",
" mi = [m[i][j] for i,j in zip(study_id, measure_id)]\n",
" \n",
" age_effect = np.array([alpha_age[a-1] if a else 0 for a in age_cat])\n",
" \n",
" return(mi + baseline_sd*(betas + age_effect))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Expected treatment effect for pre-K undergoing multi-component intervention, measused by Eyberg Child Behaviour Inventory, Intensity Subscale"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if wishart:\n",
" baseline = pm.MvNormal('baseline', mu, T, value=[0]*k)\n",
"else:\n",
" baseline = pm.MvNormalCov('baseline', mu, Sigma, value=[0]*k)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculation of expected differences from baseline for each instrument."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# ECBI intensity subscale\n",
"ecbi_intensity_sd = baseline_sd[np.array(measure_id)==0].mean()\n",
"\n",
"prek_intensity_pred = pm.Lambda('prek_intensity_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[0] + ecbi_intensity_sd*(b + a[0]) )\n",
"school_intensity_pred = pm.Lambda('school_intensity_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[0] + ecbi_intensity_sd*b )\n",
"teen_intensity_pred = pm.Lambda('teen_intensity_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[0] + ecbi_intensity_sd*(b + a[1]) )\n",
"\n",
"intensity_diff = pm.Lambda('intensity_diff', \n",
" lambda base=baseline, prek=prek_intensity_pred, school=school_intensity_pred, \n",
" teen=teen_intensity_pred: np.array([prek, school, teen])-base[0])\n",
" \n",
"\n",
"# ECBI problem subscale\n",
"ecbi_problem_sd = baseline_sd[np.array(measure_id)==1].mean()\n",
"\n",
"prek_problem_pred = pm.Lambda('prek_problem_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[1] + ecbi_problem_sd*(b + a[0]) )\n",
"school_problem_pred = pm.Lambda('school_problem_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[1] + ecbi_problem_sd*b )\n",
"teen_problem_pred = pm.Lambda('teen_problem_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[1] + ecbi_problem_sd*(b + a[1]) )\n",
"\n",
"problem_diff = pm.Lambda('problem_diff', \n",
" lambda base=baseline, prek=prek_problem_pred, school=school_problem_pred, \n",
" teen=teen_problem_pred: np.array([prek, school, teen])-base[1])\n",
"\n",
"# CBC T-score\n",
"cbct_sd = baseline_sd[np.array(measure_id)==2].mean()\n",
"\n",
"prek_tscore_pred = pm.Lambda('prek_tscore_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[2] + cbct_sd*(b + a[0]) )\n",
"school_tscore_pred = pm.Lambda('school_tscore_pred', \n",
" lambda mu=baseline, b=mu_beta: mu[2] + cbct_sd*b )\n",
"teen_tscore_pred = pm.Lambda('teen_tscore_pred', \n",
" lambda mu=baseline, a=alpha_age, b=mu_beta: mu[2] + cbct_sd*(b + a[1]) )\n",
"\n",
"tscore_diff = pm.Lambda('tscore_diff', \n",
" lambda base=baseline, prek=prek_tscore_pred, school=school_tscore_pred, \n",
" teen=teen_tscore_pred: np.array([prek, school, teen])-base[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the likelihood is just a normal distribution, with the observed standard error of the treatment effect as the standard deviation of the estimates.\n",
"\n",
"$$d_i \\sim N(\\theta_i, \\hat{\\sigma}^2)$$"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"change_se = change_sd/np.sqrt(change_n)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = pm.Normal('d', theta, change_se**-2, observed=True, value=change_mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Posterior predictive samples, if desired."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"include_gof_samples = False\n",
"\n",
"if include_gof_samples:\n",
" d_sim = pm.Normal('d_sim', theta, change_se**-2, size=len(change_mean))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate MCMC model, and use Adaptive Metropolis for some vector-valued variables."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M = pm.MCMC(locals())\n",
"M.use_step_method(pm.AdaptiveMetropolis, m)\n",
"M.use_step_method(pm.AdaptiveMetropolis, rhos)\n",
"M.use_step_method(pm.AdaptiveMetropolis, sigmas)\n",
"M.use_step_method(pm.AdaptiveMetropolis, mu_beta)\n",
"M.use_step_method(pm.AdaptiveMetropolis, [beta_c, beta_p, beta_m])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run two chains to allow for convergence evaluation."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 500000 of 500000 complete in 1336.1 sec"
]
}
],
"source": [
"M.sample(500000, 490000)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 500000 of 500000 complete in 1346.1 sec"
]
}
],
"source": [
"M.sample(500000, 490000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Summary of estimates of intervention components"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if wishart:\n",
" pm.Matplot.plot(T)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting mu_beta_0\n",
"Plotting mu_beta_1\n",
"Plotting mu_beta_2\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115d72ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(mu_beta)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115984eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot([mu_beta], custom_labels=['Child component', 'Parent component', \n",
" 'Multi-component'], chain=0, xlab=\"Effect size (standard deviations)\")"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11dd9e3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot([mu_beta], custom_labels=['Child component', 'Parent component', \n",
" 'Multi-component'])"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"mu_beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t-1.015 0.274 0.013 [-1.553 -0.497]\n",
"\t-1.205 0.164 0.007 [-1.51 -0.853]\n",
"\t-1.281 0.18 0.009 [-1.654 -0.946]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-1.54 -1.208 -1.019 -0.823 -0.478\n",
"\t-1.537 -1.31 -1.202 -1.097 -0.879\n",
"\t-1.636 -1.4 -1.282 -1.159 -0.914\n",
"\t\n"
]
}
],
"source": [
"mu_beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mu_trace = M.mu_beta.trace()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"multi_child = mu_trace[:, 2] - mu_trace[:, 0]\n",
"multi_parent = mu_trace[:, 2] - mu_trace[:, 1]\n",
"parent_child = mu_trace[:, 1] - mu_trace[:, 0]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Multi-component vs child-only: mean=-0.24, 95% HPD=(-0.85, 0.4)'"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Multi-component vs child-only: mean={0:.2f}, 95% HPD=({1:.2}, {2:.2})'.format(multi_child.mean(), \n",
" *pm.utils.hpd(multi_child, 0.05))"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Multi-component vs parent-only: mean=-0.08, 95% HPD=(-0.55, 0.36)'"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Multi-component vs parent-only: mean={0:.2f}, 95% HPD=({1:.2}, {2:.2})'.format(multi_parent.mean(), \n",
" *pm.utils.hpd(multi_parent, 0.05))"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Parent vs child-only: mean=-0.16, 95% HPD=(-0.85, 0.4)'"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Parent vs child-only: mean={0:.2f}, 95% HPD=({1:.2}, {2:.2})'.format(parent_child.mean(), \n",
" *pm.utils.hpd(parent_child, 0.05))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting mu_beta_0\n",
"Plotting mu_beta_1\n",
"Plotting mu_beta_2\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11e0876d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(mu_beta)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if interaction:\n",
" pm.Matplot.summary_plot([beta_pk_m, beta_pk_p])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Difference means by measure instrument."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115ddb908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(24,4))\n",
"pm.Matplot.summary_plot([mu], custom_labels=unique_measures)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting mu_0\n",
"Plotting mu_1\n",
"Plotting mu_2\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11dbac9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(mu)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115ca26a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if not wishart:\n",
" plt.figure(figsize=(24,4))\n",
" pm.Matplot.summary_plot([sigmas], custom_labels=unique_measures)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"sigmas:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.076 0.038 0.004 [ 0.03 0.131]\n",
"\t0.075 0.025 0.003 [ 0.045 0.124]\n",
"\t0.025 0.013 0.001 [ 0.01 0.044]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t0.023 0.038 0.105 0.105 0.129\n",
"\t0.042 0.05 0.086 0.086 0.124\n",
"\t0.01 0.013 0.024 0.037 0.048\n",
"\t\n"
]
}
],
"source": [
"sigmas.summary()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting sigmas_0\n",
"Plotting sigmas_1\n",
"Plotting sigmas_2\n"
]
},
{
"data": {
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"YII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11dfc8c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(sigmas)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115a40d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if not wishart:\n",
" pm.Matplot.summary_plot([rhos], custom_labels=['rho12', 'rho13', 'rho23'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Age effects for pre-k (top) and teen (bottom) groups, relative to pre-teen."
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11e97f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.summary_plot([alpha_age], custom_labels=['pre-K', 'teen'])"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"alpha_age:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t-0.461 0.072 0.003 [-0.597 -0.318]\n",
"\t-0.012 0.222 0.012 [-0.45 0.395]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-0.6 -0.512 -0.462 -0.414 -0.319\n",
"\t-0.443 -0.169 -0.008 0.141 0.406\n",
"\t\n"
]
}
],
"source": [
"alpha_age.summary()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting alpha_age_0\n",
"Plotting alpha_age_1\n"
]
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x115f59c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.Matplot.plot(alpha_age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Outcome Plots"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"traces = [[school_intensity_pred, prek_intensity_pred, teen_intensity_pred],\n",
" [school_problem_pred, prek_problem_pred, teen_problem_pred],\n",
" [school_tscore_pred, prek_tscore_pred, teen_tscore_pred]]"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x1159e0240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1159e02b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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