FlatNormalPosterior: Integrand to compute Bayesian Predictive Power when flat...

In bpp: Computations Around Bayesian Predictive Power

Description

Assume we have a flat prior on our effect, update it with a Normal likelihood and then want to compute Bayesian Predictive Power. This function provides the integrand for that computation, i.e. the product of the power function and the posterior.

Usage

FlatNormalPosterior(x, successmean, finalSE, interimmean, interimSE, priormean, 
                           width, height)

Arguments

x	Value at which to evaluate the function.
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.
interimmean	Mean of the data.
interimSE	(Known) standard error of interimmean.
priormean	Prior mean.
width	Width of the flat part of the prior.
height	Height of the flat part of the prior.

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_1interim for code of all the computations in Rufibach et al (2016a).

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