bpp: Bayesian Predictive Power (BPP) for Normally Distributed...

In bpp: Computations Around Bayesian Predictive Power

Description

Compute BPP for a Normally distributed endpoint, e.g. log(hazard ratio).

Usage

bpp(prior = c("normal", "flat"), successmean, finalSE, priormean, ...)

Arguments

prior	Prior density on effect sizes.
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.
finalSE	(Known) standard error at which the final analysis of the study under consideration takes place.
priormean	Prior mean.
...	Further arguments specific to the chosen prior (see bpp for examples).

Value

A real number, the bpp.

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.

Rufibach, K., Burger, H.U., Abt, M. (2016b). Bayesian Predictive Power: Choice of Prior and some Recommendations for its Use as Probability of Success in Drug Development. Pharm. Stat., 15, 438–446.

Examples

# type ?bpp_1interim for code of all the computations in Rufibach et al (2016a).

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