In kkholst/mets: Analysis of Multivariate Event Times

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(mets)

While Alive estimands for Recurrent Events

We consider two while-alive estimands for recurrent events data \begin{align} \frac{E(N(D \wedge t))}{E(D \wedge t)} \end{align} and the mean of the subject specific events per time-unit \begin{align} E( \frac{N(D \wedge t)}{D \wedge t} ) \end{align} for two treatment-groups in the case of an RCT. For the laste mean of events per time-unit it has been seen that when the sample size is to great it can improve the finite sample properties to employ a transformation such as $\sqrt$ or cube-root, and thus consider \begin{align} E( (\frac{N(D \wedge t)}{D \wedge t})^.33 ) \end{align}

data(hfaction_cpx12)

dtable(hfaction_cpx12,~status)
dd <- WA_recurrent(Event(entry,time,status)~treatment+cluster(id),hfaction_cpx12,time=2,death.code=2)
summary(dd)

dd <- WA_recurrent(Event(entry,time,status)~treatment+cluster(id),hfaction_cpx12,time=2,death.code=2,trans=.333)
summary(dd,type="log")

We see that the ratio of means are not very different, but that the subject specific mean of events per time-unit shows that those on the active treatment has fewer events per time-unit on average.