prior_tte: Prior distribution for time-to-event outcomes
Description
If we do not assume the treatment effects to be fixed, i.e. fixed = FALSE,
the function prior_tte allows us to model the treatment effect following a prior distribution.
For more details concerning the definition of a prior distribution, see the vignette on priors
as well as the Shiny app.
Usage
prior_tte(x, w, hr1, hr2, id1, id2)
Value
The output of the functions Epgo_tte() is the expected number of participants in phase III with conservative decision rule and sample size calculation.
Arguments
x
integration variable
w
weight for mixture prior distribution
hr1
first assumed true treatment effect on HR scale for prior distribution
hr2
second assumed true treatment effect on HR scale for prior distribution
id1
amount of information for hr1 in terms of number of events
id2
amount of information for hr2 in terms of number of events