98 lines
3.6 KiB
R
98 lines
3.6 KiB
R
|
# A reprise of tt.R, using (time1, time2) data.
|
||
|
|
library(survival)
|
||
|
|
library(splines)
|
||
|
|
aeq <- function(x, y) all.equal(as.vector(x), as.vector(y))
|
||
|
|
|
||
|
|
# A contrived example for the tt function
|
||
|
|
#
|
||
|
|
mkdata <- function(n, beta) {
|
||
|
|
age <- round(runif(n, 20, 60))
|
||
|
|
x <- rbinom(n, 1, .5)
|
||
|
|
|
||
|
|
futime <- rep(40, n) # everyone has 40 years of follow-up
|
||
|
|
entry <- pmax(0, seq(-10, 30, length=n)) # 1/4 enter at 0
|
||
|
|
entry <- round(entry)
|
||
|
|
status <- rep(0, n)
|
||
|
|
dtime <- runif(n/2, 1, 40) # 1/2 of them die
|
||
|
|
dtime <- sort(dtime)
|
||
|
|
|
||
|
|
# The risk is set to beta[1]*x + beta[2]* f(current_age)
|
||
|
|
# where f= 0 up to age 40, rises linear to age 70, flat after that
|
||
|
|
for (i in 1:length(dtime)) {
|
||
|
|
atrisk <- (futime >= dtime[i] & entry < dtime[i])
|
||
|
|
c.age <- age + dtime
|
||
|
|
age2 <- pmin(30, pmax(0, c.age-40))
|
||
|
|
xbeta <- beta[1]*x + beta[2]*age2
|
||
|
|
|
||
|
|
# Select a death according to risk
|
||
|
|
risk <- ifelse(atrisk, exp(xbeta), 0)
|
||
|
|
dead <- sample(1:n, 1, prob=risk/sum(risk))
|
||
|
|
|
||
|
|
futime[dead] <- dtime[i]
|
||
|
|
status[dead] <- 1
|
||
|
|
}
|
||
|
|
out <- data.frame(time1= entry, time2=round(futime,1), status=status,
|
||
|
|
age=age, x=x, risk=risk,
|
||
|
|
casewt = sample(1:5, n, replace=TRUE),
|
||
|
|
grp = sample(1:15, n, replace=TRUE), id= 1:n)
|
||
|
|
subset(out, time1 < time2)
|
||
|
|
}
|
||
|
|
|
||
|
|
set.seed(1953) # a good year
|
||
|
|
# Make n larger for the (time1, time2) case; more stress.
|
||
|
|
tdata <- mkdata(250, c(log(1.5), 2/30)) # data set has many ties
|
||
|
|
#tdata <- mkdata(100, c(log(1.5), 2/30)) # data set has many ties
|
||
|
|
tdata$strat <- floor(tdata$grp/10)
|
||
|
|
|
||
|
|
dtime <- sort(unique(tdata$time2[tdata$status==1]))
|
||
|
|
data2 <- survSplit(Surv(time1, time2, status) ~., tdata, cut=dtime)
|
||
|
|
data2$c.age <- data2$age + data2$time2 # current age
|
||
|
|
|
||
|
|
# fit1 uses data at the event times, fit2$c.age might have a
|
||
|
|
# wider range due to censorings. To make the two fits agree
|
||
|
|
# fix the knots. I know a priori that 20 to 101 will cover it.
|
||
|
|
ns2 <- function(x) ns(x, Boundary.knots=c(20, 101), knots=c(45, 60, 75))
|
||
|
|
|
||
|
|
fit1 <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata,
|
||
|
|
tt= function(x, t, ...) ns2(x+t))
|
||
|
|
|
||
|
|
fit2 <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2)
|
||
|
|
|
||
|
|
aeq(coef(fit1), coef(fit2))
|
||
|
|
aeq(vcov(fit1), vcov(fit2))
|
||
|
|
|
||
|
|
#
|
||
|
|
# Check that cluster, weight, and offset were correctly expanded
|
||
|
|
#
|
||
|
|
fit3a <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata, weights=casewt,
|
||
|
|
tt= function(x, t, ...) ns2(x+t), x=TRUE)
|
||
|
|
fit3b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2,
|
||
|
|
weights=casewt)
|
||
|
|
aeq(coef(fit3a), coef(fit3b))
|
||
|
|
aeq(vcov(fit3a), vcov(fit3b))
|
||
|
|
|
||
|
|
fit4a <- coxph(Surv(time1, time2, status)~ x + tt(age), tdata,
|
||
|
|
tt= function(x, t, ...) ns2(x+t), cluster=grp)
|
||
|
|
fit4b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age), data2,
|
||
|
|
cluster=grp)
|
||
|
|
fit4c <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age) + cluster(grp),
|
||
|
|
data2)
|
||
|
|
aeq(coef(fit4a), coef(fit4b))
|
||
|
|
aeq(vcov(fit4a), vcov(fit4b))
|
||
|
|
aeq(coef(fit4a), coef(fit4c))
|
||
|
|
aeq(vcov(fit4a), vcov(fit4c))
|
||
|
|
|
||
|
|
fit5a <- coxph(Surv(time1, time2, status)~ x + tt(age) + offset(grp/10), tdata,
|
||
|
|
tt= function(x, t, ...) ns2(x+t),)
|
||
|
|
fit5b <- coxph(Surv(time1, time2, status) ~ x + ns2(c.age)+ offset(grp/10)
|
||
|
|
, data=data2)
|
||
|
|
aeq(coef(fit5a), coef(fit5b))
|
||
|
|
aeq(vcov(fit5a), vcov(fit5b))
|
||
|
|
|
||
|
|
# Check that strata is correct
|
||
|
|
fit6a <- coxph(Surv(time1, time2, status) ~ x + tt(age) + strata(strat), tdata,
|
||
|
|
tt = function(x, t, ...) (x+t)^2, x=TRUE)
|
||
|
|
fit6b <- coxph(Surv(time1, time2, status) ~ x + I(c.age^2) +strata(strat), data2)
|
||
|
|
aeq(coef(fit6a), coef(fit6b))
|
||
|
|
aeq(vcov(fit6a), vcov(fit6b))
|