## load data
data(bmt)
bmt = transform(bmt, d4=d2+d3)
A = as.numeric(bmt$group>1)
bmt$A = A
## simple model fitting and plotting
library(survival)
fit = tteICE(Surv(t2,d4,type = "mstate")~A, data=bmt)
plot_inc(fit)
## model fitting using competing risk data
fit1 = surv.tteICE(A, bmt$t2, bmt$d4, 'treatment')
## plot asymptotic confidence intervals based on explicit formulas
plot_inc(fit1, ylim=c(0,1),
plot.configs=list(legend=c('AML','ALL'), show.p.value=FALSE) )
## plot bootstrap confidence intervals
fit2 = surv.tteICE(A, bmt$t2, bmt$d4, 'treatment', nboot=50)
plot_inc(fit2, ylim=c(0,1),
plot.configs=list(legend=c('AML','ALL')))
## model fitting using semicompeting risk data
fit3 = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2, "composite")
## plot asymptotic confidence intervals based on explicit formulas
plot_inc(fit3, ylim=c(0,1), plot.configs=list(add.null.line=FALSE))
## plot bootstrap confidence intervals
fit4 = scr.tteICE(A, bmt$t1, bmt$d1, bmt$t2, bmt$d2,
"composite", nboot=50) ##??
plot_inc(fit4, ylim=c(0,1),
plot.configs=list(lty=2, lwd=3, main="My title"))
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