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bpp (version 1.0.4)

Computations Around Bayesian Predictive Power

Description

Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.

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Version

Install

install.packages('bpp')

Monthly Downloads

226

Version

1.0.4

License

GPL (>= 2)

Maintainer

Kaspar Rufibach

Last Published

January 13th, 2022

Functions in bpp (1.0.4)

bpp-package

Tools for Computation of Bayesian Predictive Power for a Normally Distributed Endpoint with Known Variance
bpp

Bayesian Predictive Power (BPP) for Normally Distributed Endpoint
estimate_posterior

Posterior density conditional on known interim result
interval_toIntegrate2

Product of posterior density and conditional power for blinded interim result
bpp_2interim

Bayesian Predictive Power (BPP) for Normally Distributed Endpoint
estimate_posterior_nominator

Posterior density conditional on interim result is proportional to the value of this function
bpp_1interim

Bayesian Predictive Power (BPP) for Normally Distributed Endpoint
bpp_1interim_continuous

Bayesian Predictive Power (BPP) for Continuous Endpoint
bpp_t2e

Bayesian Predictive Power (BPP) for Time-To-Event Endpoint
bpp_continuous

Bayesian Predictive Power (BPP) for Continuous Endpoint
bpp_1interim_t2e

Bayesian Predictive Power (BPP) for Time-to-Event Endpoint
FlatNormalPosterior

Integrand to compute Bayesian Predictive Power when flat prior has been updated with likelihood
bpp_1interim_binary

Bayesian Predictive Power (BPP) for Binary Endpoint
NormalNormalPosterior

Normal-Normal Posterior in conjugate normal model, for known sigma
interval_posterior_nominator

Posterior density conditional on interim result, only known as interval, is proportional to the value of this function
estimate_toIntegrate

Product of posterior density and conditional power for known interim result
interval_toIntegrate

Product of posterior density and conditional power for blinded interim result
post_power

Conditional power conditioning on a blinded interim
interval_posterior_nominator2

Posterior density conditional on two interim results, both only known as intervals, is proportional to the value of this function
UniformNormalTails

Density and CDF for Uniform Distribution with Normal tails
basicPlot

Basic plot functions to illustrate prior and posterior densities when considering a time-to-event endpoint
bpp_binary

Bayesian Predictive Power (BPP) for Binary Endpoint