source("http://192.38.117.59/~linearpredictors/datafiles/readMelanoma.R")

melanoma$timeint <- cut(melanoma$years, c(0, 2.5, 5, Inf),
                        right = F, include.lower=T)

## all people contribute to first time interval
melext <- melanoma
melext$time <- factor(levels(melanoma$timeint)[1])
## how many years each person contributes
melext$pyrs <- sapply(melanoma$years, function(x)min(x, 2.5))
## whether they had an event during this interval
melext$case <- (melext$dc != 2)*(melext$years < 2.5)

## people that contribute to second interval are now duplicated
## and the same variables are created...
melext2 <- subset(melanoma, years > 2.5) 
melext2$time <- factor(levels(melext2$timeint)[2])
melext2$pyrs <- sapply(melext2$years, function(x)min(x - 2.5, 2.5))
melext2$case <- (melext2$dc != 2)*(melext2$years < 5)

## attach the "new" observations
melext <- rbind(melext, melext2)

## third interval, which is again the same
melext3 <- subset(melanoma, years > 5) 
melext3$time <- factor(levels(melext3$timeint)[3])
melext3$pyrs <- melext3$years - 5
melext3$case <- (melext3$dc != 2)
melext <- rbind(melext, melext3)

pois <- glm(case ~ offset(log(pyrs)) + time + sex + ulc +
            thick + I(age/10) -1,
            family = poisson, data = melext)

## We now do the same analysis, using 4 as the only cutpoint of time


## first interval, all people
m <- melanoma
m$time <- factor("<= 4")
m$pyrs <- sapply(m$years, function(x)min(x, 4))
## whether they had an event during this interval
m$case <- (m$dc != 2)*(m$years <= 4)

## second and last interval
m4 <- subset(melanoma, years > 4) 
m4$time <- factor("> 4")
m4$pyrs <- m4$years - 4
m4$case <- (m4$dc != 2)
m <- rbind(m, m4)

pois4 <- glm(case ~ offset(log(pyrs)) + time + sex + ulc +
             thick + I(age/10) -1,
             family = poisson, data = m)