PSM_1219_d2_oi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = treatment ~ female + age + pim2 + pelod + p_bloodculture + \n",
       "    comorbidity + rf_pneumonia + rf_sepsis + rf_aspiration + \n",
       "    rf_transfusion + rf_trauma + rf_drowning + MODS + d2_oi, \n",
       "    family = binomial(), data = mydata)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.8234  -0.6937  -0.5249   0.7186   2.4828  \n",
       "\n",
       "Coefficients:\n",
       "                 Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)     -3.302825   0.655808  -5.036 4.75e-07 ***\n",
       "female           0.322249   0.300782   1.071    0.284    \n",
       "age              0.003452   0.033778   0.102    0.919    \n",
       "pim2             0.009458   0.009337   1.013    0.311    \n",
       "pelod            0.019158   0.018607   1.030    0.303    \n",
       "p_bloodculture   0.475819   0.406994   1.169    0.242    \n",
       "comorbidity     -0.032601   0.317498  -0.103    0.918    \n",
       "rf_pneumonia     0.592276   0.516553   1.147    0.252    \n",
       "rf_sepsis       -0.646540   0.401143  -1.612    0.107    \n",
       "rf_aspiration    0.069461   0.729313   0.095    0.924    \n",
       "rf_transfusion   1.566685   1.131675   1.384    0.166    \n",
       "rf_trauma      -13.718811 704.236116  -0.019    0.984    \n",
       "rf_drowning      0.039825   1.013214   0.039    0.969    \n",
       "MODS            -0.187545   0.339319  -0.553    0.580    \n",
       "d2_oi            0.114407   0.016645   6.873 6.26e-12 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 388.95  on 306  degrees of freedom\n",
       "Residual deviance: 298.55  on 292  degrees of freedom\n",
       "AIC: 328.55\n",
       "\n",
       "Number of Fisher Scoring iterations: 14\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]]\n",
      "[1] 0.8255792\n",
      "\n",
      "                            Stratified by treatment\n",
      "                             0             1             p      test\n",
      "  n                            206           101                    \n",
      "  female (mean (sd))          0.44 (0.50)   0.53 (0.50)   0.126     \n",
      "  age (mean (sd))             4.04 (4.78)   4.04 (4.31)   0.998     \n",
      "  pim2 (mean (sd))           15.51 (20.65) 19.82 (22.91)  0.099     \n",
      "  pelod (mean (sd))           9.44 (10.19) 11.04 (10.09)  0.196     \n",
      "  p_bloodculture (mean (sd))  0.16 (0.36)   0.20 (0.40)   0.351     \n",
      "  comorbidity (mean (sd))     0.45 (0.50)   0.57 (0.50)   0.043     \n",
      "  rf_pneumonia (mean (sd))    0.80 (0.40)   0.84 (0.37)   0.341     \n",
      "  rf_sepsis (mean (sd))       0.30 (0.46)   0.30 (0.46)   0.987     \n",
      "  rf_aspiration (mean (sd))   0.05 (0.22)   0.04 (0.20)   0.725     \n",
      "  rf_transfusion (mean (sd))  0.01 (0.10)   0.02 (0.14)   0.465     \n",
      "  rf_trauma (mean (sd))       0.02 (0.14)   0.00 (0.00)   0.160     \n",
      "  rf_drowning (mean (sd))     0.04 (0.20)   0.03 (0.17)   0.554     \n",
      "  MODS (mean (sd))            0.40 (0.49)   0.50 (0.50)   0.107     \n",
      "  d2_oi (mean (sd))          10.64 (8.51)  23.34 (14.67) <0.001     \n",
      "  early_HFV (mean (sd))       0.00 (0.00)   1.00 (0.00)  <0.001     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "matchit(formula = treatment ~ female + age + pim2 + pelod + p_bloodculture + \n",
       "    comorbidity + rf_pneumonia + rf_sepsis + rf_aspiration + \n",
       "    rf_transfusion + rf_trauma + rf_drowning + MODS, data = mydata_wth_pscore, \n",
       "    method = \"nearest\", distance = mydata_wth_pscore$pscore, \n",
       "    caliper = 0.05, ratio = 1)\n",
       "\n",
       "Summary of balance for all data:\n",
       "               Means Treated Means Control SD Control Mean Diff eQQ Med\n",
       "distance              0.5214        0.2347     0.1731    0.2867  0.2816\n",
       "female                0.5347        0.4417     0.4978    0.0929  0.0000\n",
       "age                   4.0360        4.0374     4.7778   -0.0013  0.5400\n",
       "pim2                 19.8198       15.5146    20.6492    4.3052  1.4000\n",
       "pelod                11.0396        9.4417    10.1911    1.5979  1.0000\n",
       "p_bloodculture        0.1980        0.1553     0.3631    0.0427  0.0000\n",
       "comorbidity           0.5743        0.4515     0.4989    0.1228  0.0000\n",
       "rf_pneumonia          0.8416        0.7961     0.4039    0.0455  0.0000\n",
       "rf_sepsis             0.2970        0.2961     0.4577    0.0009  0.0000\n",
       "rf_aspiration         0.0396        0.0485     0.2154   -0.0089  0.0000\n",
       "rf_transfusion        0.0198        0.0097     0.0983    0.0101  0.0000\n",
       "rf_trauma             0.0000        0.0194     0.1383   -0.0194  0.0000\n",
       "rf_drowning           0.0297        0.0437     0.2049   -0.0140  0.0000\n",
       "MODS                  0.4950        0.3981     0.4907    0.0970  0.0000\n",
       "               eQQ Mean eQQ Max\n",
       "distance         0.2881  0.4881\n",
       "female           0.0990  1.0000\n",
       "age              0.6479  3.3500\n",
       "pim2             4.5545 21.4000\n",
       "pelod            1.8911  9.0000\n",
       "p_bloodculture   0.0396  1.0000\n",
       "comorbidity      0.1287  1.0000\n",
       "rf_pneumonia     0.0495  1.0000\n",
       "rf_sepsis        0.0000  0.0000\n",
       "rf_aspiration    0.0099  1.0000\n",
       "rf_transfusion   0.0099  1.0000\n",
       "rf_trauma        0.0198  1.0000\n",
       "rf_drowning      0.0099  1.0000\n",
       "MODS             0.0990  1.0000\n",
       "\n",
       "\n",
       "Summary of balance for matched data:\n",
       "               Means Treated Means Control SD Control Mean Diff eQQ Med\n",
       "distance              0.3853        0.3822     0.2169    0.0030  0.0052\n",
       "female                0.5079        0.5079     0.5040    0.0000  0.0000\n",
       "age                   4.3044        4.3930     4.9011   -0.0886  0.2000\n",
       "pim2                 18.4238       17.9270    23.2546    0.4968  1.2000\n",
       "pelod                11.2381        9.9524    10.8247    1.2857  1.0000\n",
       "p_bloodculture        0.1270        0.2222     0.4191   -0.0952  0.0000\n",
       "comorbidity           0.5079        0.6032     0.4932   -0.0952  0.0000\n",
       "rf_pneumonia          0.8254        0.8254     0.3827    0.0000  0.0000\n",
       "rf_sepsis             0.3016        0.3810     0.4895   -0.0794  0.0000\n",
       "rf_aspiration         0.0476        0.0476     0.2147    0.0000  0.0000\n",
       "rf_transfusion        0.0000        0.0317     0.1767   -0.0317  0.0000\n",
       "rf_trauma             0.0000        0.0000     0.0000    0.0000  0.0000\n",
       "rf_drowning           0.0317        0.0159     0.1260    0.0159  0.0000\n",
       "MODS                  0.5238        0.4921     0.5040    0.0317  0.0000\n",
       "               eQQ Mean eQQ Max\n",
       "distance         0.0054   0.012\n",
       "female           0.0000   0.000\n",
       "age              0.3810   2.580\n",
       "pim2             3.6683  26.400\n",
       "pelod            2.3333   9.000\n",
       "p_bloodculture   0.0952   1.000\n",
       "comorbidity      0.0952   1.000\n",
       "rf_pneumonia     0.0000   0.000\n",
       "rf_sepsis        0.0794   1.000\n",
       "rf_aspiration    0.0000   0.000\n",
       "rf_transfusion   0.0317   1.000\n",
       "rf_trauma        0.0000   0.000\n",
       "rf_drowning      0.0159   1.000\n",
       "MODS             0.0317   1.000\n",
       "\n",
       "Percent Balance Improvement:\n",
       "               Mean Diff. eQQ Med  eQQ Mean  eQQ Max\n",
       "distance          98.9459 98.1679   98.1301  97.5481\n",
       "female           100.0000  0.0000  100.0000 100.0000\n",
       "age            -6514.5626 62.9630   41.2039  22.9851\n",
       "pim2              88.4600 14.2857   19.4579 -23.3645\n",
       "pelod             19.5351  0.0000  -23.3857   0.0000\n",
       "p_bloodculture  -123.1446  0.0000 -140.4762   0.0000\n",
       "comorbidity       22.4453  0.0000   26.0073   0.0000\n",
       "rf_pneumonia     100.0000  0.0000  100.0000 100.0000\n",
       "rf_sepsis      -8590.8939  0.0000      -Inf     -Inf\n",
       "rf_aspiration    100.0000  0.0000  100.0000 100.0000\n",
       "rf_transfusion  -214.5276  0.0000 -220.6349   0.0000\n",
       "rf_trauma        100.0000  0.0000  100.0000 100.0000\n",
       "rf_drowning      -13.4893  0.0000  -60.3175   0.0000\n",
       "MODS              67.2692  0.0000   67.9365   0.0000\n",
       "\n",
       "Sample sizes:\n",
       "          Control Treated\n",
       "All           206     101\n",
       "Matched        63      63\n",
       "Unmatched     143      38\n",
       "Discarded       0       0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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wDGYd6zbhbmPP4BAyXL8vLly5OSkrZs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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"To identify the units, use first mouse button; to stop, use second.\"\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "integer(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oDZKbSQHk3JI/YeR0XDb3r4iJwa/EtH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\nAIOEEAX/B+b5T0ShcD8EAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Distribution of Propensity Scores”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Stratified by treatment\n",
      "                             0             1             p      test\n",
      "  n                             63            63                    \n",
      "  female (mean (sd))          0.51 (0.50)   0.51 (0.50)   1.000     \n",
      "  age (mean (sd))             4.39 (4.90)   4.30 (4.65)   0.917     \n",
      "  pim2 (mean (sd))           17.93 (23.25) 18.42 (20.56)  0.899     \n",
      "  pelod (mean (sd))           9.95 (10.82) 11.24 (9.50)   0.480     \n",
      "  p_bloodculture (mean (sd))  0.22 (0.42)   0.13 (0.34)   0.162     \n",
      "  comorbidity (mean (sd))     0.60 (0.49)   0.51 (0.50)   0.286     \n",
      "  rf_pneumonia (mean (sd))    0.83 (0.38)   0.83 (0.38)   1.000     \n",
      "  rf_sepsis (mean (sd))       0.38 (0.49)   0.30 (0.46)   0.351     \n",
      "  rf_aspiration (mean (sd))   0.05 (0.21)   0.05 (0.21)   1.000     \n",
      "  rf_transfusion (mean (sd))  0.03 (0.18)   0.00 (0.00)   0.156     \n",
      "  rf_trauma (mean (sd))       0.00 (0.00)   0.00 (0.00)   NaN       \n",
      "  rf_drowning (mean (sd))     0.02 (0.13)   0.03 (0.18)   0.563     \n",
      "  MODS (mean (sd))            0.49 (0.50)   0.52 (0.50)   0.724     \n",
      "  d2_oi (mean (sd))          17.08 (11.11) 17.42 (10.32)  0.859     \n",
      "  early_HFV (mean (sd))       0.00 (0.00)   1.00 (0.00)  <0.001     \n"
     ]
    },
    {
     "data": {
      "image/png": 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w6pY8lwWYAZ6fvMlq3J2HTYQEi1y06nzbqzXd7lcTem39ojjdawY1e0/z5unUI4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",
      "text/plain": [
       "Plot with title “Matched Control”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Load packages and data\n",
    "library(tableone)\n",
    "library(MatchIt)\n",
    "library(Matching)\n",
    "library(pROC)\n",
    "library(ROCR)\n",
    "library(ggplot2)\n",
    "library(epitools)\n",
    "library(dplyr)\n",
    "\n",
    "library(gtools)\n",
    "\n",
    "####loading KKH ICU data\n",
    "kkh_data <- read.csv(\"PICUARD_DATA_1219.csv\")\n",
    "df<-data.frame(kkh_data)\n",
    "kkh<-df[!is.na(df$d2_oi),]\n",
    "kkh <- kkh[which(((kkh$hfv==1)&(kkh$early_HFV == 1))| ((kkh$hfv==0))), ]\n",
    "####check column names\n",
    "#colnames(kkh, do.NULL = FALSE)\n",
    "\n",
    "####select features\n",
    "ID<-kkh$Study.ID\n",
    "female<-as.numeric(kkh$Gender=='Female')\n",
    "age<-kkh$Age\n",
    "pim2<-kkh$pim2\n",
    "pelod<-kkh$pelod\n",
    "p_bloodculture<-as.numeric(kkh$Positive_blood_culture=='Yes')\n",
    "early_HFV<-kkh$early_HFV\n",
    "\n",
    "#comorbidity\n",
    "comorbidity<-kkh$comorbidity\n",
    "\n",
    "#risk factor of ARDS\n",
    "rf_pneumonia<-as.numeric(kkh$PNEUMONIA=='Checked')\n",
    "rf_sepsis<-as.numeric(kkh$SEPSIS=='Checked')\n",
    "rf_aspiration<-as.numeric(kkh$ASPIRATION=='Checked')\n",
    "rf_transfusion<-as.numeric(kkh$TRANSFUSION=='Checked')\n",
    "rf_trauma<-as.numeric(kkh$TRAUMA=='Checked')\n",
    "rf_drowning<-as.numeric(kkh$DROWNING=='Checked')\n",
    "\n",
    "#other useful features\n",
    "#cardiac_failure<-as.numeric(kkh$Cardiac.failure.or.fluid.overload=='Yes')\n",
    "MODS<-as.numeric(kkh$MODS=='Yes')\n",
    "#Received_pulmonary_vasodilators<-as.numeric(kkh$Received_pulmonary_vasodilators=='Yes')\n",
    "#blood gas labtest\n",
    "#D1_FiO2<-kkh$D1_FiO2\n",
    "#D1_SpO2<-kkh$D1_SpO2\n",
    "d2_oi<-kkh$d2_oi\n",
    "#treatment and mortality\n",
    "#treatment<-kkh$hfv\n",
    "treatment<-as.numeric(kkh$Received_HFV=='Yes')\n",
    "\n",
    "died<-kkh$died_in_ICU\n",
    "#mortality_100day<-kkh$mortality_100day\n",
    "#ventilation_free_day<-kkh$ventilation_free_day\n",
    "#ventilator_duration<-kkh$ventilator_duration\n",
    "#icu_duration<-kkh$icu_duration\n",
    "#picu_free_days<-kkh$picu_free_days\n",
    "\n",
    "####new dataset\n",
    "mydata<-cbind(ID,female,age,pim2,pelod,p_bloodculture,comorbidity,rf_pneumonia,rf_sepsis,rf_aspiration,rf_transfusion\n",
    "              ,rf_trauma,rf_drowning,MODS,d2_oi,early_HFV\n",
    "              ,treatment,died)\n",
    "\n",
    "mydata<-data.frame(mydata)\n",
    "xvars<-c(\"female\",\"age\",\"pim2\",\"pelod\",\"p_bloodculture\",\"comorbidity\",\"rf_pneumonia\"\n",
    "         ,\"rf_sepsis\",\"rf_aspiration\" , \"rf_transfusion\", \"rf_trauma\", \"rf_drowning\",\"MODS\",\"d2_oi\",\"early_HFV\"\n",
    "         )\n",
    "#mydata_noNA <- na.omit(mydata)\n",
    "#fit a propensity score model with logistic regression\n",
    "psmodel <-glm(treatment~female+age+pim2+pelod+p_bloodculture+comorbidity+rf_pneumonia+rf_sepsis\n",
    "                 +rf_aspiration+rf_transfusion+rf_trauma+rf_drowning+MODS+d2_oi\n",
    "                 , data = mydata,family=binomial())\n",
    "#show coefficients etc\n",
    "summary(psmodel)\n",
    "\n",
    "#create propensity score\n",
    "pscore<-psmodel$fitted.values\n",
    "#pscore<-fitted(psmodel) \n",
    "\n",
    "label<-mydata$treatment\n",
    "perf<- ROCR::prediction(pscore, label)\n",
    "psm_auc<-ROCR::performance(ROCR::prediction(pscore, label), \"auc\")@y.values[1]\n",
    "print(psm_auc)\n",
    "roc.perf = performance(perf, measure = \"tpr\", x.measure = \"fpr\")\n",
    "plot(roc.perf,col=\"#1c61b6\",percent=TRUE)\n",
    "abline(a=0, b= 1)\n",
    "\n",
    "#########################################\n",
    "#look at a table 1\n",
    "#mydata before matching\n",
    "table1_unmatched<- CreateTableOne(vars=xvars, strata=\"treatment\", data = mydata)\n",
    "## include standardized mean difference (SMD)\n",
    "print(table1_unmatched, smd=FALSE)\n",
    "\n",
    "\n",
    "#colnames(kkh, do.NULL = TRUE, prefix = \"col\")\n",
    "##########################################\n",
    "## Using the Matchit for propensity score, nearest neighbor matching\n",
    "mydata_wth_pscore<-data.frame(cbind(mydata,pscore))\n",
    "\n",
    "m.out <- matchit(treatment~female+age+pim2+pelod+p_bloodculture+comorbidity+rf_pneumonia+rf_sepsis\n",
    "                 +rf_aspiration+rf_transfusion+rf_trauma+rf_drowning+MODS, data = mydata_wth_pscore,              \n",
    "                 method = \"nearest\", distance=mydata_wth_pscore$pscore,caliper=0.05,ratio=1)\n",
    "summary(m.out)\n",
    "\n",
    "#propensity score plots\n",
    "plot(m.out, type=\"jitter\")\n",
    "plot(m.out, type=\"hist\")\n",
    "########################################\n",
    "#use the Matching package\n",
    "#do greedy matching on propensity score\n",
    "#psmatch<-Match(Tr=mydata$treatment,M=1, X=pscore,replace=FALSE,caliper=0.2)\n",
    "#matched<-mydata[unlist(psmatch[c(\"index.treated\",\"index.control\")]),]\n",
    "#summary(matched)\n",
    "########################################\n",
    "#look at Table1 after matching\n",
    "\n",
    "#Matching Table1\n",
    "#matchedtab<-CreateTableOne(vars=xvars, strata=\"treatment\",data=matched)\n",
    "#print(matchedtab, smd=TRUE)\n",
    "\n",
    "#MatchIt Table1\n",
    "table1_matched<- CreateTableOne(vars=xvars, strata=\"treatment\", data = match.data(m.out, \"all\"))\n",
    "## include standardized mean difference (SMD)\n",
    "print(table1_matched, smd=FALSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>p_score</th><th scope=col>treatment_1</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.57788005</td><td>1         </td></tr>\n",
       "\t<tr><td>0.22036708</td><td>1         </td></tr>\n",
       "\t<tr><td>0.40813167</td><td>1         </td></tr>\n",
       "\t<tr><td>0.48698849</td><td>1         </td></tr>\n",
       "\t<tr><td>0.09534551</td><td>1         </td></tr>\n",
       "\t<tr><td>0.40792885</td><td>1         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " p\\_score & treatment\\_1\\\\\n",
       "\\hline\n",
       "\t 0.57788005 & 1         \\\\\n",
       "\t 0.22036708 & 1         \\\\\n",
       "\t 0.40813167 & 1         \\\\\n",
       "\t 0.48698849 & 1         \\\\\n",
       "\t 0.09534551 & 1         \\\\\n",
       "\t 0.40792885 & 1         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "p_score | treatment_1 | \n",
       "|---|---|---|---|---|---|\n",
       "| 0.57788005 | 1          | \n",
       "| 0.22036708 | 1          | \n",
       "| 0.40813167 | 1          | \n",
       "| 0.48698849 | 1          | \n",
       "| 0.09534551 | 1          | \n",
       "| 0.40792885 | 1          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  p_score    treatment_1\n",
       "1 0.57788005 1          \n",
       "2 0.22036708 1          \n",
       "3 0.40813167 1          \n",
       "4 0.48698849 1          \n",
       "5 0.09534551 1          \n",
       "6 0.40792885 1          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HHKAYkZccsBSZxyQGJG3HJAEqcckJgR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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################################\n",
    "#checking balance using pscore plots\n",
    "\n",
    "#using Matching package\n",
    "#matched_treated<-mydata_wth_pscore[mydata_wth_pscore$ID %in% matched$ID[matched$treatment==1],]['pscore'][,1]\n",
    "#matched_control<-mydata_wth_pscore[mydata_wth_pscore$ID %in% matched$ID[matched$treatment==0],]['pscore'][,1]\n",
    "\n",
    "#using MatchIt package\n",
    "matched_treated<-mydata_wth_pscore[mydata_wth_pscore$ID %in% match.data(m.out, \"all\")$ID[match.data(m.out, \"all\")$treatment==1],]['pscore'][,1]\n",
    "matched_control<-mydata_wth_pscore[mydata_wth_pscore$ID %in% match.data(m.out, \"all\")$ID[match.data(m.out, \"all\")$treatment==0],]['pscore'][,1]\n",
    "\n",
    "pscore_outcome<-rbind(cbind(p_score=matched_treated,treatment_1=1),cbind(p_score=matched_control,treatment_1=0))\n",
    "prs_df<-data.frame(pscore_outcome)\n",
    "head(prs_df)\n",
    "###############################################\n",
    "#checking balance after matching by ploting the pscore\n",
    "\n",
    "#dataframe = data.frame(x = matched_treated, x2 = matched_control)\n",
    "#g = ggplot(dataframe, aes(x)) + geom_histogram( aes(x = x, y = ..density..),binwidth = diff(range(dataframe$x))/30, fill=\"skyblue\") + geom_histogram( aes(x = x2, y = -..density..), binwidth = diff(range(dataframe$x))/30, fill= \"lightgreen\")\n",
    "#print(g)\n",
    "\n",
    "##use histogram\n",
    "#hist(matched_treated,breaks=12,ylim=c(0,40),col='skyblue',border=F,xlab=\"Propensity Score\", main=\"Propensity Score After Matching\")\n",
    "#hist(matched_control,add=T,col=scales::alpha('red',.5),border=F)\n",
    "#x_trt<-seq(min(matched_treated),max(matched_treated),length=length(matched_treated))\n",
    "#y_trt<-dnorm(x_trt,mean=mean(matched_treated),sd=sd(matched_treated)) \n",
    "#y_trt <- y_trt*diff(h$mids[1:2])*length(matched_treated)\n",
    "#lines(x_trt, y_trt, col=\"blue\", lwd=1)\n",
    "#x_con<-seq(min(matched_control),max(matched_control),length=length(matched_control))\n",
    "#y_con<-dnorm(x_con,mean=mean(matched_control),sd=sd(matched_control)) \n",
    "#y_con <- y_con*diff(h$mids[1:2])*length(matched_control)\n",
    "#lines(x_con, y_con, col=\"red\", lwd=1)\n",
    "\n",
    "# Simulate some data. Individuals' heights in cm.\n",
    "n        <- length(matched_treated)\n",
    "mean     <- mean(matched_treated)\n",
    "sd       <- sd(matched_treated)\n",
    "binwidth <- 2\n",
    "height <- rnorm(n, mean, sd)\n",
    "\n",
    "labs <- paste(\"Propensity Score After Matching\", c(\"Control\", \"Treatment\"))\n",
    "prs_df %>%\n",
    "  mutate(treatment_1 = ifelse(treatment_1 == 1, labs[1], labs[2]))%>%\n",
    "  ggplot(aes(x = p_score)) +\n",
    "  geom_histogram(color = \"white\") +\n",
    "  facet_wrap(~treatment_1) +\n",
    "  xlab(\"Probability of Receiving High Frequency Ventilation\") +\n",
    "  theme_bw()+\n",
    "stat_function(fun = dnorm, args = list(mean = mean, sd = sd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           y_control\n",
       "y_treatment  0  1\n",
       "          0 23 18\n",
       "          1 13  9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tMcNemar's Chi-squared test with continuity correction\n",
       "\n",
       "data:  y_treatment and y_control\n",
       "McNemar's chi-squared = 0.51613, df = 1, p-value = 0.4725\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$data</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>Disease1</th><th scope=col>Disease2</th><th scope=col>Total</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>22 </td><td>41 </td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>27 </td><td>36 </td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Total</th><td>49 </td><td>77 </td><td>126</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$measure</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>estimate</th><th scope=col>lower</th><th scope=col>upper</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>1.0000000</td><td>       NA</td><td>      NA </td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>0.7181036</td><td>0.3459807</td><td>1.477812 </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$p.value</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>midp.exact</th><th scope=col>fisher.exact</th><th scope=col>chi.square</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>       NA</td><td>       NA</td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>0.3689022</td><td>0.4649826</td><td>0.3608654</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$correction</dt>\n",
       "\t\t<dd>FALSE</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$data] \\begin{tabular}{r|lll}\n",
       "  & Disease1 & Disease2 & Total\\\\\n",
       "\\hline\n",
       "\tExposed1 & 22  & 41  &  63\\\\\n",
       "\tExposed2 & 27  & 36  &  63\\\\\n",
       "\tTotal & 49  & 77  & 126\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$measure] \\begin{tabular}{r|lll}\n",
       "  & estimate & lower & upper\\\\\n",
       "\\hline\n",
       "\tExposed1 & 1.0000000 &        NA &       NA \\\\\n",
       "\tExposed2 & 0.7181036 & 0.3459807 & 1.477812 \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$p.value] \\begin{tabular}{r|lll}\n",
       "  & midp.exact & fisher.exact & chi.square\\\\\n",
       "\\hline\n",
       "\tExposed1 &        NA &        NA &        NA\\\\\n",
       "\tExposed2 & 0.3689022 & 0.4649826 & 0.3608654\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$correction] FALSE\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$data\n",
       ":   \n",
       "| <!--/--> | Disease1 | Disease2 | Total | \n",
       "|---|---|---|\n",
       "| Exposed1 | 22  | 41  |  63 | \n",
       "| Exposed2 | 27  | 36  |  63 | \n",
       "| Total | 49  | 77  | 126 | \n",
       "\n",
       "\n",
       "\n",
       "$measure\n",
       ":   \n",
       "| <!--/--> | estimate | lower | upper | \n",
       "|---|---|\n",
       "| Exposed1 | 1.0000000 |        NA |       NA  | \n",
       "| Exposed2 | 0.7181036 | 0.3459807 | 1.477812  | \n",
       "\n",
       "\n",
       "\n",
       "$p.value\n",
       ":   \n",
       "| <!--/--> | midp.exact | fisher.exact | chi.square | \n",
       "|---|---|\n",
       "| Exposed1 |        NA |        NA |        NA | \n",
       "| Exposed2 | 0.3689022 | 0.4649826 | 0.3608654 | \n",
       "\n",
       "\n",
       "\n",
       "$correction\n",
       ":   FALSE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$data\n",
       "          Outcome\n",
       "Predictor  Disease1 Disease2 Total\n",
       "  Exposed1       22       41    63\n",
       "  Exposed2       27       36    63\n",
       "  Total          49       77   126\n",
       "\n",
       "$measure\n",
       "          odds ratio with 95% C.I.\n",
       "Predictor   estimate     lower    upper\n",
       "  Exposed1 1.0000000        NA       NA\n",
       "  Exposed2 0.7181036 0.3459807 1.477812\n",
       "\n",
       "$p.value\n",
       "          two-sided\n",
       "Predictor  midp.exact fisher.exact chi.square\n",
       "  Exposed1         NA           NA         NA\n",
       "  Exposed2  0.3689022    0.4649826  0.3608654\n",
       "\n",
       "$correction\n",
       "[1] FALSE\n",
       "\n",
       "attr(,\"method\")\n",
       "[1] \"median-unbiased estimate & mid-p exact CI\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###################################\n",
    "#outcome analysis\n",
    "#Matching package\n",
    "#y_treatment<-matched1$died[matched$treatment==1]\n",
    "#y_control<-matched1$died[matched$treatment==0]\n",
    "\n",
    "#MatchIt package\n",
    "y_treatment<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==1] \n",
    "y_control<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==0] \n",
    "\n",
    "\n",
    "\n",
    "#pairwise difference for continuous data\n",
    "#diffy<-y_trt-y_con\n",
    "#paired t-test\n",
    "#t.test(diffy)\n",
    "\n",
    "table(y_treatment,y_control)\n",
    "#McNemar test\n",
    "mcnemar.test(y_treatment, y_control)\n",
    "\n",
    "########## calculate Odd's ratio here!\n",
    "a<-length(y_treatment[y_treatment==1])\n",
    "b<-length(y_control[y_control==1])\n",
    "c<-length(y_treatment[y_treatment==0])\n",
    "d<-length(y_control[y_control==0])\n",
    "\n",
    "oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$data</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>Disease1</th><th scope=col>Disease2</th><th scope=col>Total</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>26 </td><td> 60</td><td> 86</td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>37 </td><td> 49</td><td> 86</td></tr>\n",
       "\t<tr><th scope=row>Total</th><td>63 </td><td>109</td><td>172</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$measure</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>estimate</th><th scope=col>lower</th><th scope=col>upper</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>1.0000000</td><td>       NA</td><td>      NA </td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>0.5765563</td><td>0.3046182</td><td>1.078876 </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$p.value</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>midp.exact</th><th scope=col>fisher.exact</th><th scope=col>chi.square</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Exposed1</th><td>        NA</td><td>       NA </td><td>        NA</td></tr>\n",
       "\t<tr><th scope=row>Exposed2</th><td>0.08515071</td><td>0.1131678 </td><td>0.08170142</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$correction</dt>\n",
       "\t\t<dd>FALSE</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$data] \\begin{tabular}{r|lll}\n",
       "  & Disease1 & Disease2 & Total\\\\\n",
       "\\hline\n",
       "\tExposed1 & 26  &  60 &  86\\\\\n",
       "\tExposed2 & 37  &  49 &  86\\\\\n",
       "\tTotal & 63  & 109 & 172\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$measure] \\begin{tabular}{r|lll}\n",
       "  & estimate & lower & upper\\\\\n",
       "\\hline\n",
       "\tExposed1 & 1.0000000 &        NA &       NA \\\\\n",
       "\tExposed2 & 0.5765563 & 0.3046182 & 1.078876 \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$p.value] \\begin{tabular}{r|lll}\n",
       "  & midp.exact & fisher.exact & chi.square\\\\\n",
       "\\hline\n",
       "\tExposed1 &         NA &        NA  &         NA\\\\\n",
       "\tExposed2 & 0.08515071 & 0.1131678  & 0.08170142\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$correction] FALSE\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$data\n",
       ":   \n",
       "| <!--/--> | Disease1 | Disease2 | Total | \n",
       "|---|---|---|\n",
       "| Exposed1 | 26  |  60 |  86 | \n",
       "| Exposed2 | 37  |  49 |  86 | \n",
       "| Total | 63  | 109 | 172 | \n",
       "\n",
       "\n",
       "\n",
       "$measure\n",
       ":   \n",
       "| <!--/--> | estimate | lower | upper | \n",
       "|---|---|\n",
       "| Exposed1 | 1.0000000 |        NA |       NA  | \n",
       "| Exposed2 | 0.5765563 | 0.3046182 | 1.078876  | \n",
       "\n",
       "\n",
       "\n",
       "$p.value\n",
       ":   \n",
       "| <!--/--> | midp.exact | fisher.exact | chi.square | \n",
       "|---|---|\n",
       "| Exposed1 |         NA |        NA  |         NA | \n",
       "| Exposed2 | 0.08515071 | 0.1131678  | 0.08170142 | \n",
       "\n",
       "\n",
       "\n",
       "$correction\n",
       ":   FALSE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$data\n",
       "          Outcome\n",
       "Predictor  Disease1 Disease2 Total\n",
       "  Exposed1       26       60    86\n",
       "  Exposed2       37       49    86\n",
       "  Total          63      109   172\n",
       "\n",
       "$measure\n",
       "          odds ratio with 95% C.I.\n",
       "Predictor   estimate     lower    upper\n",
       "  Exposed1 1.0000000        NA       NA\n",
       "  Exposed2 0.5765563 0.3046182 1.078876\n",
       "\n",
       "$p.value\n",
       "          two-sided\n",
       "Predictor  midp.exact fisher.exact chi.square\n",
       "  Exposed1         NA           NA         NA\n",
       "  Exposed2 0.08515071    0.1131678 0.08170142\n",
       "\n",
       "$correction\n",
       "[1] FALSE\n",
       "\n",
       "attr(,\"method\")\n",
       "[1] \"median-unbiased estimate & mid-p exact CI\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##############################################\n",
    "\n",
    "m.out <- matchit(treatment~female+age+pim2+pelod+p_bloodculture+comorbidity+rf_pneumonia+rf_sepsis\n",
    "                 +rf_aspiration+rf_transfusion+rf_trauma+rf_drowning+MODS, data = mydata, \n",
    "                 method = \"nearest\", distance=mydata_wth_pscore$pscore,caliper=0.1)\n",
    "\n",
    "#psmatch<-Match(Tr=mydata$treatment,M=1, X=pscore,replace=FALSE,caliper=0.01)\n",
    "#matched<-mydata[unlist(psmatch[c(\"index.treated\",\"index.control\")]),]\n",
    "\n",
    "#MatchIt package\n",
    "y_treatment<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==1] \n",
    "y_control<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==0] \n",
    "\n",
    "#Matching package\n",
    "#y_treatment<-matched$died[matched$treatment==1]\n",
    "#y_control<-matched$died[matched$treatment==0]\n",
    "\n",
    "a<-length(y_treatment[y_treatment==1])\n",
    "b<-length(y_control[y_control==1])\n",
    "c<-length(y_treatment[y_treatment==0])\n",
    "d<-length(y_control[y_control==0])\n",
    "\n",
    "oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in chisq.test(xx, correct = correction):\n",
      "“Chi-squared approximation may be incorrect”Warning message in chisq.test(xx, correct = correction):\n",
      "“Chi-squared approximation may be incorrect”Warning message in chisq.test(xx, correct = correction):\n",
      "“Chi-squared approximation may be incorrect”Warning message in chisq.test(xx, correct = correction):\n",
      "“Chi-squared approximation may be incorrect”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”Warning message in if (distance %in% c(\"GAMlogit\", \"GAMprobit\", \"GAMcloglog\", \"GAMlog\", :\n",
      "“the condition has length > 1 and only the first element will be used”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>calipers</th><th scope=col>odds</th><th scope=col>ci5</th><th scope=col>ci95</th><th scope=col>p_value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.001     </td><td>0.3876245 </td><td>0.03598798</td><td>3.023440  </td><td>0.3755656 </td></tr>\n",
       "\t<tr><td>0.010     </td><td>0.7476178 </td><td>0.30709567</td><td>1.799295  </td><td>0.5165056 </td></tr>\n",
       "\t<tr><td>0.020     </td><td>1.0000033 </td><td>0.45478167</td><td>2.198857  </td><td>1.0000000 </td></tr>\n",
       "\t<tr><td>0.040     </td><td>0.9333524 </td><td>0.44507768</td><td>1.953402  </td><td>0.8544925 </td></tr>\n",
       "\t<tr><td>0.060     </td><td>0.7713931 </td><td>0.37499168</td><td>1.575805  </td><td>0.4766135 </td></tr>\n",
       "\t<tr><td>0.080     </td><td>0.7747056 </td><td>0.37909494</td><td>1.572959  </td><td>0.4801680 </td></tr>\n",
       "\t<tr><td>0.100     </td><td>1.1334225 </td><td>0.56158318</td><td>2.294882  </td><td>0.7264283 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lllll}\n",
       " calipers & odds & ci5 & ci95 & p\\_value\\\\\n",
       "\\hline\n",
       "\t 0.001      & 0.3876245  & 0.03598798 & 3.023440   & 0.3755656 \\\\\n",
       "\t 0.010      & 0.7476178  & 0.30709567 & 1.799295   & 0.5165056 \\\\\n",
       "\t 0.020      & 1.0000033  & 0.45478167 & 2.198857   & 1.0000000 \\\\\n",
       "\t 0.040      & 0.9333524  & 0.44507768 & 1.953402   & 0.8544925 \\\\\n",
       "\t 0.060      & 0.7713931  & 0.37499168 & 1.575805   & 0.4766135 \\\\\n",
       "\t 0.080      & 0.7747056  & 0.37909494 & 1.572959   & 0.4801680 \\\\\n",
       "\t 0.100      & 1.1334225  & 0.56158318 & 2.294882   & 0.7264283 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "calipers | odds | ci5 | ci95 | p_value | \n",
       "|---|---|---|---|---|---|---|\n",
       "| 0.001      | 0.3876245  | 0.03598798 | 3.023440   | 0.3755656  | \n",
       "| 0.010      | 0.7476178  | 0.30709567 | 1.799295   | 0.5165056  | \n",
       "| 0.020      | 1.0000033  | 0.45478167 | 2.198857   | 1.0000000  | \n",
       "| 0.040      | 0.9333524  | 0.44507768 | 1.953402   | 0.8544925  | \n",
       "| 0.060      | 0.7713931  | 0.37499168 | 1.575805   | 0.4766135  | \n",
       "| 0.080      | 0.7747056  | 0.37909494 | 1.572959   | 0.4801680  | \n",
       "| 0.100      | 1.1334225  | 0.56158318 | 2.294882   | 0.7264283  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     calipers odds      ci5        ci95     p_value  \n",
       "[1,] 0.001    0.3876245 0.03598798 3.023440 0.3755656\n",
       "[2,] 0.010    0.7476178 0.30709567 1.799295 0.5165056\n",
       "[3,] 0.020    1.0000033 0.45478167 2.198857 1.0000000\n",
       "[4,] 0.040    0.9333524 0.44507768 1.953402 0.8544925\n",
       "[5,] 0.060    0.7713931 0.37499168 1.575805 0.4766135\n",
       "[6,] 0.080    0.7747056 0.37909494 1.572959 0.4801680\n",
       "[7,] 0.100    1.1334225 0.56158318 2.294882 0.7264283"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################################\n",
    "\n",
    "calipers<-c(0.001,0.01,0.02,0.04,0.06,0.08,0.1)\n",
    "caliper_0.05<-c(0.05,0.05,0.05,0.05,0.05)\n",
    "odds<-c()\n",
    "ci5<-c()\n",
    "ci95<-c()\n",
    "p_value<-c()\n",
    "\n",
    "for(i in calipers){\n",
    "    \n",
    "    m.out <- matchit(treatment~female+age+pim2+pelod+p_bloodculture+comorbidity+rf_pneumonia+rf_sepsis\n",
    "                 +rf_aspiration+rf_transfusion+rf_trauma+rf_drowning+MODS, data = mydata_wth_pscore, \n",
    "                 method = \"nearest\", distance=mydata_wth_pscore$pscore,caliper=i,ratio=1)\n",
    "\n",
    "    #Matching package\n",
    "    y_treatment<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==1] \n",
    "    y_control<-match.data(m.out)$died[match.data(m.out, \"all\")$treatment==0] \n",
    "\n",
    "\n",
    "    a<-length(y_treatment[y_treatment==1])\n",
    "    b<-length(y_control[y_control==1])\n",
    "    c<-length(y_treatment[y_treatment==0])\n",
    "    d<-length(y_control[y_control==0])\n",
    "\n",
    "    odds<-c(odds,oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2),verbose = FALSE)$measure[2,1])\n",
    "    ci5<-c(ci5,oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2),verbose = FALSE)$measure[2,2])\n",
    "    ci95<-c(ci95,oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2),verbose = FALSE)$measure[2,3])\n",
    "    p_value<-c(p_value,oddsratio(matrix(c(a,b,c,d), ncol=2, nrow=2),verbose = FALSE)$p.value[2,1])\n",
    "    \n",
    "\n",
    "    \n",
    "}\n",
    "\n",
    "outcome_table<-cbind(calipers,odds,ci5,ci95,p_value)\n",
    "outcome_table\n",
    "\n",
    "\n",
    "#exp(cbind(Odds_and_OR=coef(psmodel), confint(psmodel)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bRo6eZ6RhESMkxqQ6qa3N73kvOuM/ba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\nQEiABoQEaEBIgAaEBGjw/137cQZ00vBNAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Scatter plot with std.dev error bars”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(calipers, odds,\n",
    "    ylim=range(c(ci5, ci95)),\n",
    "    pch=19, xlab=\"Calipers Measurements\", ylab=\"odds (CI: 5%-95%)\",\n",
    "    main=\"Scatter plot with std.dev error bars\"\n",
    ")\n",
    "# hack: we draw arrows but with very special \"arrowheads\"\n",
    "arrows(calipers, ci5, calipers, ci95, length=0.05, angle=90, code=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3w3YH5uwzIjJFPYp8dMSOGP1LpPNTtx/KGq7dKIt2gqQjKFfXoMhrRX20X+y0cT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\nx9l9hCSLfXqc3UdIstinx9l9hCSLfXqc3UdIstinx9l9hCSLfXq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      "text/plain": [
       "Plot with title “Scatter plot with std.dev error bars”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( odds,\n",
    "    ylim=range(c(ci5, ci95)),\n",
    "    pch=19, xlab=\"Calipers 0.05\", ylab=\"odds (CI: 5%-95%)\",\n",
    "    main=\"Scatter plot with std.dev error bars\"\n",
    ")\n",
    "# hack: we draw arrows but with very special \"arrowheads\"\n",
    "arrows(c(1,2,3,4,5), ci5, c(1,2,3,4,5), ci95, length=0.05, angle=90, code=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>matchedID_hfv</th><th scope=col>matchedID_non_hfv</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1006</td><td>1001</td></tr>\n",
       "\t<tr><td>1009</td><td>1005</td></tr>\n",
       "\t<tr><td>1022</td><td>1010</td></tr>\n",
       "\t<tr><td>1024</td><td>1015</td></tr>\n",
       "\t<tr><td>1035</td><td>1023</td></tr>\n",
       "\t<tr><td>1037</td><td>1049</td></tr>\n",
       "\t<tr><td>1043</td><td>1050</td></tr>\n",
       "\t<tr><td>1046</td><td>1057</td></tr>\n",
       "\t<tr><td>1047</td><td>1058</td></tr>\n",
       "\t<tr><td>1048</td><td>1059</td></tr>\n",
       "\t<tr><td>1053</td><td>1063</td></tr>\n",
       "\t<tr><td>1065</td><td>1064</td></tr>\n",
       "\t<tr><td>1070</td><td>1066</td></tr>\n",
       "\t<tr><td>1093</td><td>1071</td></tr>\n",
       "\t<tr><td>1098</td><td>1073</td></tr>\n",
       "\t<tr><td>1109</td><td>1075</td></tr>\n",
       "\t<tr><td>1119</td><td>1077</td></tr>\n",
       "\t<tr><td>2002</td><td>1078</td></tr>\n",
       "\t<tr><td>2003</td><td>1081</td></tr>\n",
       "\t<tr><td>2005</td><td>1082</td></tr>\n",
       "\t<tr><td>2008</td><td>1084</td></tr>\n",
       "\t<tr><td>2011</td><td>1089</td></tr>\n",
       "\t<tr><td>2018</td><td>1090</td></tr>\n",
       "\t<tr><td>2020</td><td>1096</td></tr>\n",
       "\t<tr><td>2023</td><td>1100</td></tr>\n",
       "\t<tr><td>2027</td><td>1101</td></tr>\n",
       "\t<tr><td>2033</td><td>1104</td></tr>\n",
       "\t<tr><td>3001</td><td>1105</td></tr>\n",
       "\t<tr><td>3002</td><td>1106</td></tr>\n",
       "\t<tr><td>3004</td><td>1107</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td> 7004</td><td> 8004</td></tr>\n",
       "\t<tr><td> 7041</td><td> 8010</td></tr>\n",
       "\t<tr><td> 7045</td><td> 8011</td></tr>\n",
       "\t<tr><td> 8021</td><td> 8013</td></tr>\n",
       "\t<tr><td> 8022</td><td> 8014</td></tr>\n",
       "\t<tr><td> 8029</td><td> 8015</td></tr>\n",
       "\t<tr><td> 8030</td><td> 8016</td></tr>\n",
       "\t<tr><td> 8031</td><td> 8017</td></tr>\n",
       "\t<tr><td> 9006</td><td> 8019</td></tr>\n",
       "\t<tr><td> 9009</td><td> 8020</td></tr>\n",
       "\t<tr><td> 9010</td><td> 8027</td></tr>\n",
       "\t<tr><td> 9011</td><td> 8028</td></tr>\n",
       "\t<tr><td> 9012</td><td> 8033</td></tr>\n",
       "\t<tr><td> 9026</td><td> 8034</td></tr>\n",
       "\t<tr><td> 9035</td><td> 9007</td></tr>\n",
       "\t<tr><td> 9065</td><td> 9025</td></tr>\n",
       "\t<tr><td> 9073</td><td> 9038</td></tr>\n",
       "\t<tr><td> 9077</td><td> 9048</td></tr>\n",
       "\t<tr><td>11001</td><td> 9053</td></tr>\n",
       "\t<tr><td>11002</td><td> 9069</td></tr>\n",
       "\t<tr><td>11003</td><td> 9074</td></tr>\n",
       "\t<tr><td>11004</td><td> 9080</td></tr>\n",
       "\t<tr><td>11005</td><td>10002</td></tr>\n",
       "\t<tr><td>11009</td><td>10005</td></tr>\n",
       "\t<tr><td>11010</td><td>10007</td></tr>\n",
       "\t<tr><td>11012</td><td>11011</td></tr>\n",
       "\t<tr><td>11015</td><td>11013</td></tr>\n",
       "\t<tr><td>11016</td><td>11014</td></tr>\n",
       "\t<tr><td>11018</td><td>11017</td></tr>\n",
       "\t<tr><td>11020</td><td>11019</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       " matchedID\\_hfv & matchedID\\_non\\_hfv\\\\\n",
       "\\hline\n",
       "\t 1006 & 1001\\\\\n",
       "\t 1009 & 1005\\\\\n",
       "\t 1022 & 1010\\\\\n",
       "\t 1024 & 1015\\\\\n",
       "\t 1035 & 1023\\\\\n",
       "\t 1037 & 1049\\\\\n",
       "\t 1043 & 1050\\\\\n",
       "\t 1046 & 1057\\\\\n",
       "\t 1047 & 1058\\\\\n",
       "\t 1048 & 1059\\\\\n",
       "\t 1053 & 1063\\\\\n",
       "\t 1065 & 1064\\\\\n",
       "\t 1070 & 1066\\\\\n",
       "\t 1093 & 1071\\\\\n",
       "\t 1098 & 1073\\\\\n",
       "\t 1109 & 1075\\\\\n",
       "\t 1119 & 1077\\\\\n",
       "\t 2002 & 1078\\\\\n",
       "\t 2003 & 1081\\\\\n",
       "\t 2005 & 1082\\\\\n",
       "\t 2008 & 1084\\\\\n",
       "\t 2011 & 1089\\\\\n",
       "\t 2018 & 1090\\\\\n",
       "\t 2020 & 1096\\\\\n",
       "\t 2023 & 1100\\\\\n",
       "\t 2027 & 1101\\\\\n",
       "\t 2033 & 1104\\\\\n",
       "\t 3001 & 1105\\\\\n",
       "\t 3002 & 1106\\\\\n",
       "\t 3004 & 1107\\\\\n",
       "\t ⋮ & ⋮\\\\\n",
       "\t  7004 &  8004\\\\\n",
       "\t  7041 &  8010\\\\\n",
       "\t  7045 &  8011\\\\\n",
       "\t  8021 &  8013\\\\\n",
       "\t  8022 &  8014\\\\\n",
       "\t  8029 &  8015\\\\\n",
       "\t  8030 &  8016\\\\\n",
       "\t  8031 &  8017\\\\\n",
       "\t  9006 &  8019\\\\\n",
       "\t  9009 &  8020\\\\\n",
       "\t  9010 &  8027\\\\\n",
       "\t  9011 &  8028\\\\\n",
       "\t  9012 &  8033\\\\\n",
       "\t  9026 &  8034\\\\\n",
       "\t  9035 &  9007\\\\\n",
       "\t  9065 &  9025\\\\\n",
       "\t  9073 &  9038\\\\\n",
       "\t  9077 &  9048\\\\\n",
       "\t 11001 &  9053\\\\\n",
       "\t 11002 &  9069\\\\\n",
       "\t 11003 &  9074\\\\\n",
       "\t 11004 &  9080\\\\\n",
       "\t 11005 & 10002\\\\\n",
       "\t 11009 & 10005\\\\\n",
       "\t 11010 & 10007\\\\\n",
       "\t 11012 & 11011\\\\\n",
       "\t 11015 & 11013\\\\\n",
       "\t 11016 & 11014\\\\\n",
       "\t 11018 & 11017\\\\\n",
       "\t 11020 & 11019\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "matchedID_hfv | matchedID_non_hfv | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1006 | 1001 | \n",
       "| 1009 | 1005 | \n",
       "| 1022 | 1010 | \n",
       "| 1024 | 1015 | \n",
       "| 1035 | 1023 | \n",
       "| 1037 | 1049 | \n",
       "| 1043 | 1050 | \n",
       "| 1046 | 1057 | \n",
       "| 1047 | 1058 | \n",
       "| 1048 | 1059 | \n",
       "| 1053 | 1063 | \n",
       "| 1065 | 1064 | \n",
       "| 1070 | 1066 | \n",
       "| 1093 | 1071 | \n",
       "| 1098 | 1073 | \n",
       "| 1109 | 1075 | \n",
       "| 1119 | 1077 | \n",
       "| 2002 | 1078 | \n",
       "| 2003 | 1081 | \n",
       "| 2005 | 1082 | \n",
       "| 2008 | 1084 | \n",
       "| 2011 | 1089 | \n",
       "| 2018 | 1090 | \n",
       "| 2020 | 1096 | \n",
       "| 2023 | 1100 | \n",
       "| 2027 | 1101 | \n",
       "| 2033 | 1104 | \n",
       "| 3001 | 1105 | \n",
       "| 3002 | 1106 | \n",
       "| 3004 | 1107 | \n",
       "| ⋮ | ⋮ | \n",
       "|  7004 |  8004 | \n",
       "|  7041 |  8010 | \n",
       "|  7045 |  8011 | \n",
       "|  8021 |  8013 | \n",
       "|  8022 |  8014 | \n",
       "|  8029 |  8015 | \n",
       "|  8030 |  8016 | \n",
       "|  8031 |  8017 | \n",
       "|  9006 |  8019 | \n",
       "|  9009 |  8020 | \n",
       "|  9010 |  8027 | \n",
       "|  9011 |  8028 | \n",
       "|  9012 |  8033 | \n",
       "|  9026 |  8034 | \n",
       "|  9035 |  9007 | \n",
       "|  9065 |  9025 | \n",
       "|  9073 |  9038 | \n",
       "|  9077 |  9048 | \n",
       "| 11001 |  9053 | \n",
       "| 11002 |  9069 | \n",
       "| 11003 |  9074 | \n",
       "| 11004 |  9080 | \n",
       "| 11005 | 10002 | \n",
       "| 11009 | 10005 | \n",
       "| 11010 | 10007 | \n",
       "| 11012 | 11011 | \n",
       "| 11015 | 11013 | \n",
       "| 11016 | 11014 | \n",
       "| 11018 | 11017 | \n",
       "| 11020 | 11019 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "      matchedID_hfv matchedID_non_hfv\n",
       " [1,] 1006          1001             \n",
       " [2,] 1009          1005             \n",
       " [3,] 1022          1010             \n",
       " [4,] 1024          1015             \n",
       " [5,] 1035          1023             \n",
       " [6,] 1037          1049             \n",
       " [7,] 1043          1050             \n",
       " [8,] 1046          1057             \n",
       " [9,] 1047          1058             \n",
       "[10,] 1048          1059             \n",
       "[11,] 1053          1063             \n",
       "[12,] 1065          1064             \n",
       "[13,] 1070          1066             \n",
       "[14,] 1093          1071             \n",
       "[15,] 1098          1073             \n",
       "[16,] 1109          1075             \n",
       "[17,] 1119          1077             \n",
       "[18,] 2002          1078             \n",
       "[19,] 2003          1081             \n",
       "[20,] 2005          1082             \n",
       "[21,] 2008          1084             \n",
       "[22,] 2011          1089             \n",
       "[23,] 2018          1090             \n",
       "[24,] 2020          1096             \n",
       "[25,] 2023          1100             \n",
       "[26,] 2027          1101             \n",
       "[27,] 2033          1104             \n",
       "[28,] 3001          1105             \n",
       "[29,] 3002          1106             \n",
       "[30,] 3004          1107             \n",
       "[31,] ⋮             ⋮                \n",
       "[32,]  7004          8004            \n",
       "[33,]  7041          8010            \n",
       "[34,]  7045          8011            \n",
       "[35,]  8021          8013            \n",
       "[36,]  8022          8014            \n",
       "[37,]  8029          8015            \n",
       "[38,]  8030          8016            \n",
       "[39,]  8031          8017            \n",
       "[40,]  9006          8019            \n",
       "[41,]  9009          8020            \n",
       "[42,]  9010          8027            \n",
       "[43,]  9011          8028            \n",
       "[44,]  9012          8033            \n",
       "[45,]  9026          8034            \n",
       "[46,]  9035          9007            \n",
       "[47,]  9065          9025            \n",
       "[48,]  9073          9038            \n",
       "[49,]  9077          9048            \n",
       "[50,] 11001          9053            \n",
       "[51,] 11002          9069            \n",
       "[52,] 11003          9074            \n",
       "[53,] 11004          9080            \n",
       "[54,] 11005         10002            \n",
       "[55,] 11009         10005            \n",
       "[56,] 11010         10007            \n",
       "[57,] 11012         11011            \n",
       "[58,] 11015         11013            \n",
       "[59,] 11016         11014            \n",
       "[60,] 11018         11017            \n",
       "[61,] 11020         11019            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matchedID_hfv<-match.data(m.out)$ID[match.data(m.out, \"all\")$treatment==1] \n",
    "matchedID_non_hfv<-match.data(m.out)$ID[match.data(m.out, \"all\")$treatment==0]\n",
    "pairs<-cbind(matchedID_hfv,matchedID_non_hfv)\n",
    "pairs\n",
    "write.csv(pairs, file = \"pair_ID.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "307"
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      "text/latex": [
       "307"
      ],
      "text/markdown": [
       "307"
      ],
      "text/plain": [
       "[1] 307"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length(kkh[,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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