matched case control logistic regression

matched_logistic.R

n <- 1000
g <- rbinom(n, 2, 0.5)
y <- g + rnorm(n)
yb <- rep(0, n)
yb[y >= median(y)] <- 1

dat <- data.frame(y=y, yb=yb, g=g)
dat <- dat[order(dat$yb), ]
dat$group <- rep(1:sum(yb==0), 2)


# Logistic regression fitting family as random effect
library(lme4)
model1 <- summary(glmer(yb ~ g + (1|group), family="binomial", data=dat))

# Logistic regression fitting family as fixed effects
library(lme4)
model2 <- summary(glm(yb ~ g + as.factor(group), family="binomial", data=dat))

# Using conditional logistic regression
library(survival)
model3 <- summary(clogit(yb ~ g + strata(group), data=dat ))


model1
model2
model3