### Name: pyears
### Title: Person Years
### Aliases: pyears
### Keywords: survival

### ** Examples

# Look at progression rates jointly by calendar date and age
# 
temp.yr  <- tcut(mgus$dxyr, 55:92, labels=as.character(55:91)) 
temp.age <- tcut(mgus$age, 34:101, labels=as.character(34:100))
ptime <- ifelse(is.na(mgus$pctime), mgus$futime, mgus$pctime)
pstat <- ifelse(is.na(mgus$pctime), 0, 1)
pfit <- pyears(Surv(ptime/365.25, pstat) ~ temp.yr + temp.age + sex,  mgus,
     data.frame=TRUE) 
# Turn the factor back into numerics for regression
tdata <- pfit$data
tdata$age <- as.numeric(as.character(tdata$temp.age))
tdata$year<- as.numeric(as.character(tdata$temp.yr))
fit1 <- glm(event ~ year + age+ sex +offset(log(pyears)),
             data=tdata, family=poisson)
## Not run: 
##D # fit a gam model 
##D gfit.m <- gam(y ~ s(age) + s(year) + offset(log(time)),  
##D                         family = poisson, data = tdata) 
## End(Not run)

# Example #2  Create the hearta data frame: 
hearta <- by(heart, heart$id,  
             function(x)x[x$stop == max(x$stop),]) 
hearta <- do.call("rbind", hearta) 
# Produce pyears table of death rates on the surgical arm
#  The first is by age at randomization, the second by current age
fit1 <- pyears(Surv(stop/365.25, event) ~ cut(age + 48, c(0,50,60,70,100)) + 
       surgery, data = hearta, scale = 1)
fit2 <- pyears(Surv(stop/365.25, event) ~ tcut(age + 48, c(0,50,60,70,100)) + 
       surgery, data = hearta, scale = 1)
fit1$event/fit1$pyears  #death rates on the surgery and non-surg arm

fit2$event/fit2$pyears  #death rates on the surgery and non-surg arm