# Load data
heart <- read.table("http://publicifsv.sund.ku.dk/~pka/epidata/stanford.txt", header = TRUE)
library(survival)

# dummy variables for 3 age groups
heart$age1 <- 0
heart$age2 <- 0
heart$age3 <- 0
heart$age1[heart$age <= 40] <- 1
heart$age2[40 < heart$age & heart$age  <= 50] <- 1
heart$age3[heart$age > 50] <-1 

# 1.: model with age categories
fit <- coxph(Surv(days, cens) ~age2 + age3, data = heart)
summary(fit)

# 2.: time-fixed 'effect' of trans
fit2 <- coxph(Surv(days, cens) ~trans, data = heart)
summary(fit2)

# 3.: time-dep. effect of trans

# create new dataset including information on transplantation
notrans <- heart[heart$wait <0, ]
notrans$entry <- 0
notrans$exit <- notrans$days
notrans$death <- notrans$cens
notrans$xstatus <- 0

trans <- heart[heart$wait> 0, ]
trans$entry <- 0
trans$exit <- trans$wait
trans$death <- 0
trans$xstatus <- 0

trans2 <- heart[heart$wait> 0, ]
trans2$entry <- trans2$wait
trans2$exit <- trans2$days
trans2$death <- trans2$cens
trans2$xstatus <- 1

# merge the datasets
updated <- rbind(notrans, trans, trans2)

fit3 <-  coxph(Surv(entry, exit, death) ~ xstatus, data = updated)
summary(fit3)

# 4.: include age categories
fit4 <-  coxph(Surv(entry, exit, death) ~ xstatus + age2 + age3, data = updated)
summary(fit4)

# 5. age as time variable
updated$aentry <- updated$age
updated$aexit <- updated$age + updated$exit/365

fit5 <-  coxph(Surv(aentry, aexit, death) ~ xstatus, data = updated)
summary(fit5)

# 6. (linear) adjustment for follow-up time
updated$fup <- updated$aexit - updated$aentry
fit6 <-  coxph(Surv(aentry, aexit, death) ~ xstatus + fup, data = updated)
summary(fit6)