notebooks/aline-aws/aline_propensity_score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "system(\"ln -s /home/ec2-user/anaconda3/envs/R_Beta/bin/x86_64-conda_cos6-linux-gnu-c++ /home/ec2-user/anaconda3/bin/x86_64-conda_cos6-linux-gnu-c++\")\n",
    "system(\"ln -s /home/ec2-user/anaconda3/envs/R_Beta/bin/x86_64-conda_cos6-linux-gnu-cc /home/ec2-user/anaconda3/bin/x86_64-conda_cos6-linux-gnu-cc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "install.packages('pROC')\n",
    "install.packages('Matching')\n",
    "knitr::opts_chunk$set(echo = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdpath = path.expand(\"./\")\n",
    "setwd(wdpath)\n",
    "dataset = read.csv(file=\"aline_data.csv\",head=TRUE,sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset$icustay_id = factor(dataset$icustay_id)\n",
    "dataset$day_28_flag = factor(dataset$day_28_flag, levels=c(0,1))\n",
    "dataset$gender = factor(dataset$gender, levels=c(\"F\",\"M\"))\n",
    "dataset$day_icu_intime = factor(dataset$day_icu_intime)\n",
    "dataset$hour_icu_intime = factor(dataset$hour_icu_intime)\n",
    "dataset$icu_hour_flag = factor(dataset$icu_hour_flag, levels=c(0,1))\n",
    "#dataset$sepsis_flag = factor(dataset$sepsis_flag, levels=c(0,1))\n",
    "dataset$sedative_flag = factor(dataset$sedative_flag, levels=c(0,1))\n",
    "dataset$fentanyl_flag = factor(dataset$fentanyl_flag, levels=c(0,1))\n",
    "dataset$midazolam_flag = factor(dataset$midazolam_flag, levels=c(0,1))\n",
    "dataset$propofol_flag = factor(dataset$propofol_flag, levels=c(0,1))\n",
    "#dataset$dilaudid_flag = factor(dataset$dilaudid_flag, levels=c(0,1))\n",
    "dataset$chf_flag = factor(dataset$chf_flag, levels=c(0,1))\n",
    "dataset$afib_flag = factor(dataset$afib_flag, levels=c(0,1))\n",
    "dataset$renal_flag = factor(dataset$renal_flag, levels=c(0,1))\n",
    "dataset$liver_flag = factor(dataset$liver_flag, levels=c(0,1))\n",
    "dataset$copd_flag = factor(dataset$copd_flag, levels=c(0,1))\n",
    "dataset$cad_flag = factor(dataset$cad_flag, levels=c(0,1))\n",
    "dataset$stroke_flag = factor(dataset$stroke_flag, levels=c(0,1))\n",
    "dataset$malignancy_flag = factor(dataset$malignancy_flag, levels=c(0,1))\n",
    "dataset$respfail_flag = factor(dataset$respfail_flag, levels=c(0,1))\n",
    "dataset$ards_flag = factor(dataset$ards_flag, levels=c(0,1))\n",
    "dataset$pneumonia_flag = factor(dataset$pneumonia_flag, levels=c(0,1))\n",
    "\n",
    "# custom factor\n",
    "dataset$service_surg = factor( dataset$service_unit == 'SURG', levels=c(FALSE,TRUE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we could impute data if we like - e.g. the below imputes the mean\n",
    "# we currently do complete case analysis however\n",
    "imputeFlag = 0\n",
    "if (imputeFlag != 0){\n",
    "  print(\"Imputing missing data for some features...\")\n",
    "for (col in c(\"weight_first\",\"temp_first\",\"spo2_first\",\n",
    "              \"bun_first\",\"creatinine_first\", \"chloride_first\", \"hgb_first\",\n",
    "              \"platelet_first\", \"potassium_first\", \"sodium_first\", \"tco2_first\", \"wbc_first\"))\n",
    "{\n",
    "  print(paste(\"Imputing data for: \", col))\n",
    "  dataset[is.na(dataset[,col]),col] = mean(dataset[,col], na.rm=TRUE)\n",
    "}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Removed 161 rows with missing data.\"\n"
     ]
    }
   ],
   "source": [
    "# subselect the variables\n",
    "dat = dataset[,c(\"aline_flag\",\n",
    "                  \"age\",\"gender\",\"weight_first\",\"sofa_first\",\"service_surg\",\n",
    "                  \"day_icu_intime\",\"hour_icu_intime\",\n",
    "                  \"chf_flag\",\"afib_flag\",\"renal_flag\",\n",
    "                  \"liver_flag\",\"copd_flag\",\"cad_flag\",\"stroke_flag\",\n",
    "                  \"malignancy_flag\",\"respfail_flag\",\n",
    "                  \"map_first\",\"hr_first\",\"temp_first\",\"spo2_first\",\n",
    "                  \"bun_first\",\"chloride_first\",\"creatinine_first\",\n",
    "                  \"hgb_first\",\"platelet_first\",\n",
    "                  \"potassium_first\",\"sodium_first\",\"tco2_first\",\"wbc_first\")]\n",
    "\n",
    "idxKeep = complete.cases(dat)\n",
    "dat = dat[idxKeep,]\n",
    "y <- dataset[idxKeep,\"day_28_flag\"]\n",
    "\n",
    "print(paste('Removed', sum(!idxKeep),'rows with missing data.'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit GLM\n",
    "glm_fitted = glm(aline_flag ~ ., data=dat, family=\"binomial\", na.action = na.exclude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=3053.01\n",
      "aline_flag ~ age + gender + weight_first + sofa_first + service_surg + \n",
      "    day_icu_intime + hour_icu_intime + chf_flag + afib_flag + \n",
      "    renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag + \n",
      "    malignancy_flag + respfail_flag + map_first + hr_first + \n",
      "    temp_first + spo2_first + bun_first + chloride_first + creatinine_first + \n",
      "    hgb_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                   Df Deviance    AIC\n",
      "- hour_icu_intime  23   2976.6 3044.6\n",
      "- day_icu_intime    6   2947.5 3049.5\n",
      "- creatinine_first  1   2939.1 3051.1\n",
      "- hr_first          1   2939.2 3051.2\n",
      "- afib_flag         1   2939.2 3051.2\n",
      "- temp_first        1   2939.2 3051.2\n",
      "- cad_flag          1   2939.4 3051.4\n",
      "- age               1   2939.5 3051.5\n",
      "- chf_flag          1   2939.6 3051.6\n",
      "- spo2_first        1   2939.6 3051.6\n",
      "- gender            1   2939.6 3051.6\n",
      "- malignancy_flag   1   2939.7 3051.7\n",
      "- hgb_first         1   2939.9 3051.9\n",
      "- respfail_flag     1   2940.0 3052.0\n",
      "- bun_first         1   2940.1 3052.1\n",
      "<none>                  2939.0 3053.0\n",
      "- potassium_first   1   2941.1 3053.1\n",
      "- platelet_first    1   2941.5 3053.5\n",
      "- weight_first      1   2942.5 3054.5\n",
      "- renal_flag        1   2944.2 3056.2\n",
      "- tco2_first        1   2944.3 3056.3\n",
      "- liver_flag        1   2944.6 3056.6\n",
      "- copd_flag         1   2946.6 3058.6\n",
      "- map_first         1   2952.2 3064.2\n",
      "- sodium_first      1   2960.3 3072.3\n",
      "- sofa_first        1   2964.5 3076.5\n",
      "- wbc_first         1   2968.7 3080.7\n",
      "- stroke_flag       1   2976.8 3088.8\n",
      "- chloride_first    1   2978.2 3090.2\n",
      "- service_surg      1   2990.9 3102.9\n",
      "\n",
      "Step:  AIC=3044.6\n",
      "aline_flag ~ age + gender + weight_first + sofa_first + service_surg + \n",
      "    day_icu_intime + chf_flag + afib_flag + renal_flag + liver_flag + \n",
      "    copd_flag + cad_flag + stroke_flag + malignancy_flag + respfail_flag + \n",
      "    map_first + hr_first + temp_first + spo2_first + bun_first + \n",
      "    chloride_first + creatinine_first + hgb_first + platelet_first + \n",
      "    potassium_first + sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                   Df Deviance    AIC\n",
      "- day_icu_intime    6   2982.9 3038.9\n",
      "- creatinine_first  1   2976.6 3042.6\n",
      "- hr_first          1   2976.7 3042.7\n",
      "- afib_flag         1   2976.8 3042.8\n",
      "- age               1   2976.8 3042.8\n",
      "- spo2_first        1   2976.9 3042.9\n",
      "- gender            1   2977.0 3043.0\n",
      "- temp_first        1   2977.0 3043.0\n",
      "- cad_flag          1   2977.1 3043.1\n",
      "- malignancy_flag   1   2977.2 3043.2\n",
      "- chf_flag          1   2977.2 3043.2\n",
      "- respfail_flag     1   2977.4 3043.4\n",
      "- hgb_first         1   2977.7 3043.7\n",
      "- bun_first         1   2977.7 3043.7\n",
      "- potassium_first   1   2977.9 3043.9\n",
      "<none>                  2976.6 3044.6\n",
      "- platelet_first    1   2979.5 3045.5\n",
      "- weight_first      1   2979.8 3045.8\n",
      "- renal_flag        1   2982.3 3048.3\n",
      "- tco2_first        1   2982.6 3048.6\n",
      "- liver_flag        1   2982.8 3048.8\n",
      "- copd_flag         1   2985.8 3051.8\n",
      "- map_first         1   2989.9 3055.9\n",
      "- sodium_first      1   2998.7 3064.7\n",
      "- sofa_first        1   3002.4 3068.4\n",
      "- wbc_first         1   3005.7 3071.7\n",
      "- stroke_flag       1   3014.7 3080.7\n",
      "- chloride_first    1   3017.9 3083.9\n",
      "- service_surg      1   3032.2 3098.2\n",
      "\n",
      "Step:  AIC=3038.9\n",
      "aline_flag ~ age + gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + \n",
      "    cad_flag + stroke_flag + malignancy_flag + respfail_flag + \n",
      "    map_first + hr_first + temp_first + spo2_first + bun_first + \n",
      "    chloride_first + creatinine_first + hgb_first + platelet_first + \n",
      "    potassium_first + sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                   Df Deviance    AIC\n",
      "- creatinine_first  1   2982.9 3036.9\n",
      "- hr_first          1   2983.0 3037.0\n",
      "- afib_flag         1   2983.1 3037.1\n",
      "- age               1   2983.1 3037.1\n",
      "- spo2_first        1   2983.2 3037.2\n",
      "- temp_first        1   2983.3 3037.3\n",
      "- gender            1   2983.4 3037.4\n",
      "- cad_flag          1   2983.5 3037.5\n",
      "- chf_flag          1   2983.5 3037.5\n",
      "- malignancy_flag   1   2983.6 3037.6\n",
      "- respfail_flag     1   2983.7 3037.7\n",
      "- hgb_first         1   2983.8 3037.8\n",
      "- bun_first         1   2984.2 3038.2\n",
      "- potassium_first   1   2984.4 3038.4\n",
      "<none>                  2982.9 3038.9\n",
      "- platelet_first    1   2985.6 3039.6\n",
      "- weight_first      1   2985.9 3039.9\n",
      "- renal_flag        1   2988.6 3042.6\n",
      "- tco2_first        1   2988.7 3042.7\n",
      "- liver_flag        1   2989.0 3043.0\n",
      "- copd_flag         1   2992.1 3046.1\n",
      "- map_first         1   2995.9 3049.9\n",
      "- sodium_first      1   3005.6 3059.6\n",
      "- sofa_first        1   3009.5 3063.5\n",
      "- wbc_first         1   3012.2 3066.2\n",
      "- stroke_flag       1   3020.8 3074.8\n",
      "- chloride_first    1   3024.8 3078.8\n",
      "- service_surg      1   3038.9 3092.9\n",
      "\n",
      "Step:  AIC=3036.9\n",
      "aline_flag ~ age + gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + \n",
      "    cad_flag + stroke_flag + malignancy_flag + respfail_flag + \n",
      "    map_first + hr_first + temp_first + spo2_first + bun_first + \n",
      "    chloride_first + hgb_first + platelet_first + potassium_first + \n",
      "    sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- hr_first         1   2983.0 3035.0\n",
      "- afib_flag        1   2983.1 3035.1\n",
      "- age              1   2983.1 3035.1\n",
      "- spo2_first       1   2983.2 3035.2\n",
      "- temp_first       1   2983.3 3035.3\n",
      "- gender           1   2983.4 3035.4\n",
      "- cad_flag         1   2983.5 3035.5\n",
      "- chf_flag         1   2983.5 3035.5\n",
      "- malignancy_flag  1   2983.6 3035.6\n",
      "- respfail_flag    1   2983.7 3035.7\n",
      "- hgb_first        1   2983.8 3035.8\n",
      "- potassium_first  1   2984.4 3036.4\n",
      "- bun_first        1   2984.6 3036.6\n",
      "<none>                 2982.9 3036.9\n",
      "- platelet_first   1   2985.6 3037.6\n",
      "- weight_first     1   2985.9 3037.9\n",
      "- tco2_first       1   2988.8 3040.8\n",
      "- liver_flag       1   2989.1 3041.1\n",
      "- renal_flag       1   2989.1 3041.1\n",
      "- copd_flag        1   2992.1 3044.1\n",
      "- map_first        1   2996.0 3048.0\n",
      "- sodium_first     1   3005.8 3057.8\n",
      "- sofa_first       1   3010.7 3062.7\n",
      "- wbc_first        1   3012.2 3064.2\n",
      "- stroke_flag      1   3020.8 3072.8\n",
      "- chloride_first   1   3025.9 3077.9\n",
      "- service_surg     1   3038.9 3090.9\n",
      "\n",
      "Step:  AIC=3035\n",
      "aline_flag ~ age + gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + \n",
      "    cad_flag + stroke_flag + malignancy_flag + respfail_flag + \n",
      "    map_first + temp_first + spo2_first + bun_first + chloride_first + \n",
      "    hgb_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- age              1   2983.2 3033.2\n",
      "- afib_flag        1   2983.2 3033.2\n",
      "- temp_first       1   2983.3 3033.3\n",
      "- spo2_first       1   2983.3 3033.3\n",
      "- gender           1   2983.5 3033.5\n",
      "- cad_flag         1   2983.6 3033.6\n",
      "- chf_flag         1   2983.7 3033.7\n",
      "- malignancy_flag  1   2983.7 3033.7\n",
      "- respfail_flag    1   2983.9 3033.9\n",
      "- hgb_first        1   2983.9 3033.9\n",
      "- potassium_first  1   2984.5 3034.5\n",
      "- bun_first        1   2984.7 3034.7\n",
      "<none>                 2983.0 3035.0\n",
      "- platelet_first   1   2985.7 3035.7\n",
      "- weight_first     1   2986.0 3036.0\n",
      "- tco2_first       1   2989.1 3039.1\n",
      "- renal_flag       1   2989.2 3039.2\n",
      "- liver_flag       1   2989.4 3039.4\n",
      "- copd_flag        1   2992.4 3042.4\n",
      "- map_first        1   2996.2 3046.2\n",
      "- sodium_first     1   3006.2 3056.2\n",
      "- sofa_first       1   3010.7 3060.7\n",
      "- wbc_first        1   3012.3 3062.3\n",
      "- stroke_flag      1   3021.6 3071.6\n",
      "- chloride_first   1   3026.3 3076.3\n",
      "- service_surg     1   3038.9 3088.9\n",
      "\n",
      "Step:  AIC=3033.15\n",
      "aline_flag ~ gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + \n",
      "    cad_flag + stroke_flag + malignancy_flag + respfail_flag + \n",
      "    map_first + temp_first + spo2_first + bun_first + chloride_first + \n",
      "    hgb_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- afib_flag        1   2983.2 3031.2\n",
      "- temp_first       1   2983.5 3031.5\n",
      "- spo2_first       1   2983.5 3031.5\n",
      "- gender           1   2983.8 3031.8\n",
      "- chf_flag         1   2983.8 3031.8\n",
      "- cad_flag         1   2983.9 3031.9\n",
      "- malignancy_flag  1   2983.9 3031.9\n",
      "- hgb_first        1   2984.0 3032.0\n",
      "- respfail_flag    1   2984.1 3032.1\n",
      "- potassium_first  1   2984.7 3032.7\n",
      "- bun_first        1   2984.8 3032.8\n",
      "<none>                 2983.2 3033.2\n",
      "- platelet_first   1   2985.8 3033.8\n",
      "- weight_first     1   2986.4 3034.4\n",
      "- tco2_first       1   2989.2 3037.2\n",
      "- renal_flag       1   2989.3 3037.3\n",
      "- liver_flag       1   2989.5 3037.5\n",
      "- copd_flag        1   2992.8 3040.8\n",
      "- map_first        1   2996.3 3044.3\n",
      "- sodium_first     1   3006.3 3054.3\n",
      "- sofa_first       1   3010.8 3058.8\n",
      "- wbc_first        1   3013.4 3061.4\n",
      "- stroke_flag      1   3023.3 3071.3\n",
      "- chloride_first   1   3027.1 3075.1\n",
      "- service_surg     1   3038.9 3086.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Step:  AIC=3031.24\n",
      "aline_flag ~ gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + \n",
      "    stroke_flag + malignancy_flag + respfail_flag + map_first + \n",
      "    temp_first + spo2_first + bun_first + chloride_first + hgb_first + \n",
      "    platelet_first + potassium_first + sodium_first + tco2_first + \n",
      "    wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- temp_first       1   2983.5 3029.5\n",
      "- spo2_first       1   2983.6 3029.6\n",
      "- gender           1   2983.8 3029.8\n",
      "- cad_flag         1   2983.9 3029.9\n",
      "- chf_flag         1   2984.0 3030.0\n",
      "- malignancy_flag  1   2984.0 3030.0\n",
      "- hgb_first        1   2984.1 3030.1\n",
      "- respfail_flag    1   2984.1 3030.1\n",
      "- potassium_first  1   2984.8 3030.8\n",
      "- bun_first        1   2984.9 3030.9\n",
      "<none>                 2983.2 3031.2\n",
      "- platelet_first   1   2986.0 3032.0\n",
      "- weight_first     1   2986.5 3032.5\n",
      "- renal_flag       1   2989.3 3035.3\n",
      "- tco2_first       1   2989.3 3035.3\n",
      "- liver_flag       1   2989.7 3035.7\n",
      "- copd_flag        1   2992.8 3038.8\n",
      "- map_first        1   2996.4 3042.4\n",
      "- sodium_first     1   3006.4 3052.4\n",
      "- sofa_first       1   3010.8 3056.8\n",
      "- wbc_first        1   3013.4 3059.4\n",
      "- stroke_flag      1   3025.3 3071.3\n",
      "- chloride_first   1   3027.1 3073.1\n",
      "- service_surg     1   3039.0 3085.0\n",
      "\n",
      "Step:  AIC=3029.54\n",
      "aline_flag ~ gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + \n",
      "    stroke_flag + malignancy_flag + respfail_flag + map_first + \n",
      "    spo2_first + bun_first + chloride_first + hgb_first + platelet_first + \n",
      "    potassium_first + sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- spo2_first       1   2983.9 3027.9\n",
      "- gender           1   2984.2 3028.2\n",
      "- chf_flag         1   2984.2 3028.2\n",
      "- malignancy_flag  1   2984.3 3028.3\n",
      "- cad_flag         1   2984.3 3028.3\n",
      "- hgb_first        1   2984.4 3028.4\n",
      "- respfail_flag    1   2984.4 3028.4\n",
      "- bun_first        1   2985.2 3029.2\n",
      "- potassium_first  1   2985.2 3029.2\n",
      "<none>                 2983.5 3029.5\n",
      "- platelet_first   1   2986.2 3030.2\n",
      "- weight_first     1   2986.9 3030.9\n",
      "- renal_flag       1   2989.6 3033.6\n",
      "- tco2_first       1   2989.7 3033.7\n",
      "- liver_flag       1   2989.9 3033.9\n",
      "- copd_flag        1   2993.2 3037.2\n",
      "- map_first        1   2996.5 3040.5\n",
      "- sodium_first     1   3006.6 3050.6\n",
      "- sofa_first       1   3011.1 3055.1\n",
      "- wbc_first        1   3014.3 3058.3\n",
      "- stroke_flag      1   3025.9 3069.9\n",
      "- chloride_first   1   3027.2 3071.2\n",
      "- service_surg     1   3039.6 3083.6\n",
      "\n",
      "Step:  AIC=3027.89\n",
      "aline_flag ~ gender + weight_first + sofa_first + service_surg + \n",
      "    chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + \n",
      "    stroke_flag + malignancy_flag + respfail_flag + map_first + \n",
      "    bun_first + chloride_first + hgb_first + platelet_first + \n",
      "    potassium_first + sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- gender           1   2984.5 3026.5\n",
      "- chf_flag         1   2984.6 3026.6\n",
      "- cad_flag         1   2984.7 3026.7\n",
      "- malignancy_flag  1   2984.7 3026.7\n",
      "- hgb_first        1   2984.8 3026.8\n",
      "- respfail_flag    1   2984.9 3026.9\n",
      "- bun_first        1   2985.4 3027.4\n",
      "- potassium_first  1   2985.5 3027.5\n",
      "<none>                 2983.9 3027.9\n",
      "- platelet_first   1   2986.6 3028.6\n",
      "- weight_first     1   2987.1 3029.1\n",
      "- tco2_first       1   2989.8 3031.8\n",
      "- renal_flag       1   2989.9 3031.9\n",
      "- liver_flag       1   2990.4 3032.4\n",
      "- copd_flag        1   2994.0 3036.0\n",
      "- map_first        1   2996.8 3038.8\n",
      "- sodium_first     1   3006.8 3048.8\n",
      "- sofa_first       1   3011.8 3053.8\n",
      "- wbc_first        1   3014.3 3056.3\n",
      "- stroke_flag      1   3026.3 3068.3\n",
      "- chloride_first   1   3027.4 3069.4\n",
      "- service_surg     1   3039.7 3081.7\n",
      "\n",
      "Step:  AIC=3026.53\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + chf_flag + \n",
      "    renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag + \n",
      "    malignancy_flag + respfail_flag + map_first + bun_first + \n",
      "    chloride_first + hgb_first + platelet_first + potassium_first + \n",
      "    sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- chf_flag         1   2985.1 3025.1\n",
      "- hgb_first        1   2985.1 3025.1\n",
      "- cad_flag         1   2985.2 3025.2\n",
      "- malignancy_flag  1   2985.3 3025.3\n",
      "- respfail_flag    1   2985.6 3025.6\n",
      "- potassium_first  1   2986.1 3026.1\n",
      "- bun_first        1   2986.1 3026.1\n",
      "<none>                 2984.5 3026.5\n",
      "- platelet_first   1   2987.5 3027.5\n",
      "- weight_first     1   2988.7 3028.7\n",
      "- tco2_first       1   2990.5 3030.5\n",
      "- renal_flag       1   2990.6 3030.6\n",
      "- liver_flag       1   2990.9 3030.9\n",
      "- copd_flag        1   2994.6 3034.6\n",
      "- map_first        1   2997.3 3037.3\n",
      "- sodium_first     1   3007.4 3047.4\n",
      "- sofa_first       1   3013.8 3053.8\n",
      "- wbc_first        1   3015.3 3055.3\n",
      "- stroke_flag      1   3026.3 3066.3\n",
      "- chloride_first   1   3028.4 3068.4\n",
      "- service_surg     1   3040.0 3080.0\n",
      "\n",
      "Step:  AIC=3025.09\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + cad_flag + stroke_flag + malignancy_flag + \n",
      "    respfail_flag + map_first + bun_first + chloride_first + \n",
      "    hgb_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- cad_flag         1   2985.6 3023.6\n",
      "- hgb_first        1   2985.7 3023.7\n",
      "- malignancy_flag  1   2985.9 3023.9\n",
      "- respfail_flag    1   2986.1 3024.1\n",
      "- potassium_first  1   2986.6 3024.6\n",
      "- bun_first        1   2986.9 3024.9\n",
      "<none>                 2985.1 3025.1\n",
      "- platelet_first   1   2988.1 3026.1\n",
      "- weight_first     1   2989.3 3027.3\n",
      "- renal_flag       1   2990.8 3028.8\n",
      "- tco2_first       1   2991.5 3029.5\n",
      "- liver_flag       1   2991.5 3029.5\n",
      "- copd_flag        1   2994.7 3032.7\n",
      "- map_first        1   2997.8 3035.8\n",
      "- sodium_first     1   3007.7 3045.7\n",
      "- sofa_first       1   3014.1 3052.1\n",
      "- wbc_first        1   3015.8 3053.8\n",
      "- stroke_flag      1   3026.4 3064.4\n",
      "- chloride_first   1   3028.4 3066.4\n",
      "- service_surg     1   3040.3 3078.3\n",
      "\n",
      "Step:  AIC=3023.61\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + malignancy_flag + \n",
      "    respfail_flag + map_first + bun_first + chloride_first + \n",
      "    hgb_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- hgb_first        1   2986.2 3022.2\n",
      "- malignancy_flag  1   2986.4 3022.4\n",
      "- respfail_flag    1   2986.6 3022.6\n",
      "- potassium_first  1   2987.1 3023.1\n",
      "- bun_first        1   2987.3 3023.3\n",
      "<none>                 2985.6 3023.6\n",
      "- platelet_first   1   2988.6 3024.6\n",
      "- weight_first     1   2989.8 3025.8\n",
      "- liver_flag       1   2991.9 3027.9\n",
      "- tco2_first       1   2992.0 3028.0\n",
      "- renal_flag       1   2992.2 3028.2\n",
      "- copd_flag        1   2995.3 3031.3\n",
      "- map_first        1   2998.4 3034.4\n",
      "- sodium_first     1   3007.9 3043.9\n",
      "- sofa_first       1   3015.1 3051.1\n",
      "- wbc_first        1   3016.5 3052.5\n",
      "- stroke_flag      1   3026.4 3062.4\n",
      "- chloride_first   1   3028.9 3064.9\n",
      "- service_surg     1   3040.7 3076.7\n",
      "\n",
      "Step:  AIC=3022.25\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + malignancy_flag + \n",
      "    respfail_flag + map_first + bun_first + chloride_first + \n",
      "    platelet_first + potassium_first + sodium_first + tco2_first + \n",
      "    wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- malignancy_flag  1   2986.8 3020.8\n",
      "- respfail_flag    1   2987.1 3021.1\n",
      "- potassium_first  1   2987.9 3021.9\n",
      "<none>                 2986.2 3022.2\n",
      "- bun_first        1   2988.6 3022.6\n",
      "- platelet_first   1   2989.1 3023.1\n",
      "- weight_first     1   2990.1 3024.1\n",
      "- liver_flag       1   2992.2 3026.2\n",
      "- renal_flag       1   2992.6 3026.6\n",
      "- tco2_first       1   2994.2 3028.2\n",
      "- copd_flag        1   2995.8 3029.8\n",
      "- map_first        1   2998.8 3032.8\n",
      "- sodium_first     1   3015.8 3049.8\n",
      "- sofa_first       1   3016.0 3050.0\n",
      "- wbc_first        1   3016.5 3050.5\n",
      "- stroke_flag      1   3026.5 3060.5\n",
      "- service_surg     1   3042.1 3076.1\n",
      "- chloride_first   1   3042.7 3076.7\n",
      "\n",
      "Step:  AIC=3020.83\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + respfail_flag + map_first + \n",
      "    bun_first + chloride_first + platelet_first + potassium_first + \n",
      "    sodium_first + tco2_first + wbc_first\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Df Deviance    AIC\n",
      "- respfail_flag    1   2987.7 3019.7\n",
      "- potassium_first  1   2988.5 3020.5\n",
      "<none>                 2986.8 3020.8\n",
      "- bun_first        1   2989.0 3021.0\n",
      "- platelet_first   1   2989.9 3021.9\n",
      "- weight_first     1   2990.8 3022.8\n",
      "- liver_flag       1   2992.9 3024.9\n",
      "- renal_flag       1   2993.1 3025.1\n",
      "- tco2_first       1   2994.5 3026.5\n",
      "- copd_flag        1   2996.4 3028.4\n",
      "- map_first        1   2999.5 3031.5\n",
      "- sodium_first     1   3015.8 3047.8\n",
      "- sofa_first       1   3016.2 3048.2\n",
      "- wbc_first        1   3017.8 3049.8\n",
      "- stroke_flag      1   3027.8 3059.8\n",
      "- service_surg     1   3042.2 3074.2\n",
      "- chloride_first   1   3042.9 3074.9\n",
      "\n",
      "Step:  AIC=3019.69\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + map_first + bun_first + \n",
      "    chloride_first + platelet_first + potassium_first + sodium_first + \n",
      "    tco2_first + wbc_first\n",
      "\n",
      "                  Df Deviance    AIC\n",
      "- potassium_first  1   2989.4 3019.4\n",
      "- bun_first        1   2989.6 3019.6\n",
      "<none>                 2987.7 3019.7\n",
      "- platelet_first   1   2990.8 3020.8\n",
      "- weight_first     1   2991.7 3021.7\n",
      "- liver_flag       1   2993.8 3023.8\n",
      "- renal_flag       1   2994.0 3024.0\n",
      "- tco2_first       1   2994.8 3024.8\n",
      "- copd_flag        1   2997.5 3027.5\n",
      "- map_first        1   3000.2 3030.2\n",
      "- sofa_first       1   3016.5 3046.5\n",
      "- sodium_first     1   3016.7 3046.7\n",
      "- wbc_first        1   3018.4 3048.4\n",
      "- stroke_flag      1   3030.1 3060.1\n",
      "- chloride_first   1   3043.9 3073.9\n",
      "- service_surg     1   3044.3 3074.3\n",
      "\n",
      "Step:  AIC=3019.39\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + map_first + bun_first + \n",
      "    chloride_first + platelet_first + sodium_first + tco2_first + \n",
      "    wbc_first\n",
      "\n",
      "                 Df Deviance    AIC\n",
      "- bun_first       1   2990.7 3018.7\n",
      "<none>                2989.4 3019.4\n",
      "- weight_first    1   2992.9 3020.9\n",
      "- platelet_first  1   2992.9 3020.9\n",
      "- liver_flag      1   2995.6 3023.6\n",
      "- tco2_first      1   2996.3 3024.3\n",
      "- renal_flag      1   2996.5 3024.5\n",
      "- copd_flag       1   2999.6 3027.6\n",
      "- map_first       1   3001.7 3029.7\n",
      "- sodium_first    1   3017.1 3045.1\n",
      "- sofa_first      1   3017.5 3045.5\n",
      "- wbc_first       1   3019.7 3047.7\n",
      "- stroke_flag     1   3032.5 3060.5\n",
      "- chloride_first  1   3044.4 3072.4\n",
      "- service_surg    1   3047.3 3075.3\n",
      "\n",
      "Step:  AIC=3018.65\n",
      "aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + \n",
      "    liver_flag + copd_flag + stroke_flag + map_first + chloride_first + \n",
      "    platelet_first + sodium_first + tco2_first + wbc_first\n",
      "\n",
      "                 Df Deviance    AIC\n",
      "<none>                2990.7 3018.7\n",
      "- weight_first    1   2994.3 3020.3\n",
      "- platelet_first  1   2994.4 3020.4\n",
      "- renal_flag      1   2996.5 3022.5\n",
      "- liver_flag      1   2996.9 3022.9\n",
      "- tco2_first      1   2997.5 3023.5\n",
      "- copd_flag       1   3000.7 3026.7\n",
      "- map_first       1   3003.0 3029.0\n",
      "- sodium_first    1   3018.2 3044.2\n",
      "- wbc_first       1   3021.8 3047.8\n",
      "- sofa_first      1   3024.9 3050.9\n",
      "- stroke_flag     1   3034.0 3060.0\n",
      "- chloride_first  1   3045.1 3071.1\n",
      "- service_surg    1   3048.2 3074.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Type 'citation(\"pROC\")' for a citation.\n",
      "\n",
      "Attaching package: ‘pROC’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    cov, smooth, var\n",
      "\n",
      "Setting levels: control = 0, case = 1\n",
      "Setting direction: controls < cases\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "0.695052969260227"
      ],
      "text/latex": [
       "0.695052969260227"
      ],
      "text/markdown": [
       "0.695052969260227"
      ],
      "text/plain": [
       "Area under the curve: 0.6951"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rdKZAeqDrcj7AAAjhAfr+7ddf26bbViRfn5KV8+jR2ratUMncw9UXXIEGEHAMiC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/83Ip424ssf/z8/Otr8MGBQUNGDDgiSee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\nhB0AAIBOEHYAAAA6QdgBAADoxP8DviAiMPu1SbcAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “ROC curve”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run step-wise AIC\n",
    "library(MASS);  \n",
    "glm_fitted  <- stepAIC(glm_fitted )\n",
    "\n",
    "X <- fitted(glm_fitted, type=\"response\")\n",
    "Tr <- dat$aline_flag\n",
    "\n",
    "library(\"pROC\")    \n",
    "roccurve <- roc(Tr ~ X)\n",
    "plot(roccurve, col=rainbow(7), main=\"ROC curve\", xlab=\"Specificity\", ylab=\"Sensitivity\")\n",
    "auc(roccurve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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njv3r2nn3562rRpw4cPr1ChQo76DAkJmTx58ocffmgymUTku+++Gzhw4KxZs/z8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\nrkJggg==",
      "text/plain": [
       "Plot with title “Stacked histogram of X”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot stacked histogram of the predictions\n",
    "xrange = seq(0,1,0.01)\n",
    "# 3) subset your vectors to be inside xrange\n",
    "g1 = subset(X,Tr==0)\n",
    "g2 = subset(X,Tr==1)\n",
    "\n",
    "# 4) Now, use hist to compute the counts per interval\n",
    "h1 = hist(g1,breaks=xrange,plot=F)$counts\n",
    "h2 = hist(g2,breaks=xrange,plot=F)$counts\n",
    "\n",
    "barplot(rbind(h1,h2),col=3:2,names.arg=xrange[-1],\n",
    "        legend.text=c(\"No aline\",\"Aline\"),space=0,las=1,main=\"Stacked histogram of X\")"
   ]
  },
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     "name": "stderr",
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     "text": [
      "## \n",
      "##  Matching (Version 4.9-6, Build Date: 2019-04-07)\n",
      "##  See http://sekhon.berkeley.edu/matching for additional documentation.\n",
      "##  Please cite software as:\n",
      "##   Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching\n",
      "##   Software with Automated Balance Optimization: The Matching package for R.''\n",
      "##   Journal of Statistical Software, 42(7): 1-52. \n",
      "##\n",
      "\n",
      "Warning message in Match(Y = NULL, Tr = Tr, X = X, M = 1, estimand = \"ATT\", caliper = 0.1, :\n",
      "“replace==FALSE, but there are more (weighted) treated obs than control obs.  Some treated obs will not be matched.  You may want to estimate ATC instead.”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>aline_pt</th><th scope=col>match_pt</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " aline\\_pt & match\\_pt\\\\\n",
       "\\hline\n",
       "\t 0 & 0\\\\\n",
       "\t 0 & 0\\\\\n",
       "\t 0 & 1\\\\\n",
       "\t 0 & 0\\\\\n",
       "\t 1 & 0\\\\\n",
       "\t 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| aline_pt | match_pt |\n",
       "|---|---|\n",
       "| 0 | 0 |\n",
       "| 0 | 0 |\n",
       "| 0 | 1 |\n",
       "| 0 | 0 |\n",
       "| 1 | 0 |\n",
       "| 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  aline_pt match_pt\n",
       "1 0        0       \n",
       "2 0        0       \n",
       "3 0        1       \n",
       "4 0        0       \n",
       "5 1        0       \n",
       "6 0        0       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "     Matched Control\n",
       "Aline   0   1\n",
       "    0 576 123\n",
       "    1 104  19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1.18269230769231"
      ],
      "text/latex": [
       "1.18269230769231"
      ],
      "text/markdown": [
       "1.18269230769231"
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      "text/plain": [
       "[1] 1.182692"
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    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'95% Confint 0.65'</li>\n",
       "\t<li>'95% Confint 1.1'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '95\\% Confint 0.65'\n",
       "\\item '95\\% Confint 1.1'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '95% Confint 0.65'\n",
       "2. '95% Confint 1.1'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"95% Confint 0.65\" \"95% Confint 1.1\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tMcNemar's Chi-squared test with continuity correction\n",
       "\n",
       "data:  tab.match1\n",
       "McNemar's chi-squared = 1.4273, df = 1, p-value = 0.2322\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(Matching)\n",
    "\n",
    "set.seed(43770)\n",
    "\n",
    "ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATT', caliper=0.1, exact=FALSE, replace=FALSE);\n",
    "\n",
    "# get pairs with treatment/outcome as cols\n",
    "outcome <- data.frame(aline_pt=y[ps$index.treated], match_pt=y[ps$index.control])\n",
    "head(outcome)\n",
    "\n",
    "# mcnemar's test to see if iac related to mort (test should use matched pairs)\n",
    "tab.match1 <- table(outcome$aline_pt,outcome$match_pt,dnn=c(\"Aline\",\"Matched Control\"))\n",
    "tab.match1\n",
    "tab.match1[1,2]/tab.match1[2,1]\n",
    "paste(\"95% Confint\", round(exp(c(log(tab.match1[2,1]/tab.match1[1,2]) - qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1]),log(tab.match1[2,1]/tab.match1[1,2]) + qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1])) ),2))\n",
    "mcnemar.test(tab.match1) # for 1-1 pairs"
   ]
  }
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