Dataset <- readXL("C:/../SupplementaryMaterial1.xls", rownames=FALSE, header=TRUE, na="", sheet="Sheet 1", stringsAsFactors=TRUE)
library(MatchIt)
#create a new dataset from our sheet and loads MatchIt package
#Important: GROUP have to be named "0" (for LMAb) and "1" (for LMAa)
KJAlogit<-glm(GROUP~AGE+BMI+SURGERYLENGHT, data=Dataset, family="binomial")
summary(KJAlogit)
exp(cbind(OR=coef(KJAlogit),confint(KJAlogit)))
#It returns logistic regression model with odds ration and 95% confidence intervals
Pscore<-data.frame(ps=predict(KJAlogit,type="response"),GROUP=KJAlogit$model$GROUP)
summary(Pscore)
#It returns a new dataset with propensity score calculated for each subject
final<-matchit(GROUP~AGE+BMI+SURGERYLENGHT,method="nearest",caliper=0.1,data=Dataset)
finalDataset<-match.data(final)
summary(finalDataset)
#It returns a new dataset with the matched populations