t)
> # might want to allow different shapes or scales for different events
> for (k in seq(kk)) {
> short[[paste0("time", k)]] <- rweibullPH(nn, shape=2,
> scale=exp(beta[k]*tx)*gamma)
> }
> # might want to allow censoring
>
> long <- reshape(sho
<- coxph(Surv(times) ~ tx, data=long, subset=event==1)
#summary(mod1)
mod2 <- coxph(Surv(times) ~ tx, data=long, subset=event==2)
#summary(mod2)
coef(mod)
coef(mod0)
coef(mod1)
coef(mod2) - coef(mod1)
coef(summary(mod))
coef(summary(mod0))
coef(summary(mod1))
-----Original Message---
On 7/19/19 10:19 AM, Denise b wrote:
Dear R users,
I am interested in estimating the effects of a treatment on two
time-to-event traits (on simulated data), accounting for the dependency
between the two time-to-event outcomes.
I precise that the events are NOT recurrent, NOT competitive, NO
Dear R users,
I am interested in estimating the effects of a treatment on two
time-to-event traits (on simulated data), accounting for the dependency
between the two time-to-event outcomes.
I precise that the events are NOT recurrent, NOT competitive, NOT ordered.
The individuals are NOT related
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