interval_toIntegrate2: Product of posterior density and conditional power for...

In bpp: Computations Around Bayesian Predictive Power

Description

Product of posterior density and conditional power for two blinded interim results, integrate over this function to get BPP.

Usage

interval_toIntegrate2(x, prior = "normal", interimSE, finalSE, successmean, 
                             IntEffBoundary, IntFutBoundary, priormean, ...)

Arguments

x	Value at which to evaluate the function.
prior	Prior density on effect sizes.
interimSE	(Known) standard error of interimmean, i.e. at interim analysis.
finalSE	(Known) standard error at which the final analysis of the study under consideration takes place.
successmean	The mean that defines success at the final analysis. Typically chosen to be the minimal detectable difference, i.e. the critical on the scale of the effect size of interest corresponding to the significance level at the final analysis.
IntEffBoundary	Efficacy boundary at the interim analysis.
IntFutBoundary	Futility boundary at the interim analysis.
priormean	Prior mean.
...	Further arguments specific to the chosen prior (see bpp for examples).

Value

Value of the function, a real number.

Author(s)

Kaspar Rufibach (maintainer)
[e-mail]

References

Rufibach, K., Jordan, P., Abt, M. (2016a). Sequentially Updating the Likelihood of Success of a Phase 3 Pivotal Time-to-Event Trial based on Interim Analyses or External Information. J. Biopharm. Stat., 26(2), 191–201.

Examples

# type ?bpp_2interim for code of all the computations in Rufibach et al (2016a).

bpp documentation built on Jan. 13, 2022, 5:09 p.m.