prior_tte: Prior distribution for time-to-event outcomes

In Sterniii3/drugdevelopR: Utility-Based Optimal Phase II/III Drug Development Planning

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)

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

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.

Examples

res <- prior_tte(x = 0.5, w = 0.5, hr1 = 0.69, hr2 = 0.88, id1 = 240, id2 = 420)

Sterniii3/drugdevelopR documentation built on Jan. 14, 2025, 7:50 p.m.