SIM.data.singleMarker <-
function(nn,
mu = 0,
Sigma = 1,
beta = log(3),
beta2 = log(.5),
beta3 = log(2),
lam0 = .1,
cens.lam = 0,
time.max = 5)
{
Y <- rnorm(nn, mu, Sigma)
trt <- rbinom(nn, size = 1, prob = .5)
mu.i <- Y*beta + trt*beta2 + Y*trt*beta3
#true survival time
r.ti <- log(-log(runif(nn)))
ti <- -mu.i + r.ti
ti <- exp(ti)/lam0
#time.max is the followup time.
ci = rep(time.max, nn)
if(cens.lam > 0){
ci = rexp(nn, rate = cens.lam)
}
ci = pmin(ci, time.max)
#observed marker is the min of ti and ci
xi <- pmin(ti, ci)
# failure indicator
di <- ifelse( ti == xi, 1, 0)
#xi is the min of ti and ci
#di is the indicator for failure, 1 for failure, 0 for censored
#Y is the marker values
result <- as.data.frame(cbind(xi, di, Y, trt))
names(result) = c( "xi", "di", "Y", "trt")
return(result)
}
surv_tsdata <- SIM.data.singleMarker(1000)
data(surv_tsdata)
ts_surv <- trtsel(Surv(time = xi, event = di)~Y*trt,
treatment.name = "trt",
prediction.time = 1,
data = surv_tsdata)
plot(ts_surv, bootstraps = 10)
calibrate(ts_surv)
evaluate(ts_surv, bootstraps = 10)
TreatmentSelection::evaluate(ts_surv, bootstraps = 50,bias.correct = FALSE)
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