Package: bpp 1.0.6

bpp: Computations Around Bayesian Predictive Power

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.

Authors:Kaspar Rufibach [aut, cre], Paul Jordan [aut], Markus Abt [aut]

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NEWS

# Install 'bpp' in R:
install.packages('bpp', repos = c('https://numbersman77.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.12 score 19 scripts 297 downloads 7 mentions 22 exports 1 dependencies

Last updated 15 days agofrom:88de5ef6f1. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 22 2025
R-4.5-winOKFeb 22 2025
R-4.5-macOKFeb 22 2025
R-4.5-linuxOKFeb 22 2025
R-4.4-winOKFeb 22 2025
R-4.4-macOKFeb 22 2025
R-4.3-winOKFeb 22 2025
R-4.3-macOKFeb 22 2025

Exports:basicPlotbppbpp_1interimbpp_1interim_binarybpp_1interim_continuousbpp_1interim_t2ebpp_2interimbpp_binarybpp_continuousbpp_t2edUniformNormalTailsestimate_posteriorestimate_posterior_nominatorestimate_toIntegrateFlatNormalPosteriorinterval_posterior_nominatorinterval_posterior_nominator2interval_toIntegrateinterval_toIntegrate2NormalNormalPosteriorpost_powerpUniformNormalTails

Dependencies:mvtnorm

Sequentially updating the likelihood of success of a Phase 3 pivotal time-to-event trial based on interim analyses or external information

Kaspar Rufibach, Paul Jordan, and Markus Abt

Rendered frombpp.Rmdusingknitr::rmarkdownon Feb 22 2025.

Last update: 2022-01-13
Started: 2021-05-02

Readme and manuals

Help Manual

Help pageTopics
Tools for Computation of Bayesian Predictive Power for a Normally Distributed Endpoint with Known Variancebpp-package ddcp pts
Basic plot functions to illustrate prior and posterior densities when considering a time-to-event endpointbasicPlot
Bayesian Predictive Power (BPP) for Normally Distributed Endpointbpp
Bayesian Predictive Power (BPP) for Normally Distributed Endpointbpp_1interim
Bayesian Predictive Power (BPP) for Binary Endpointbpp_1interim_binary
Bayesian Predictive Power (BPP) for Continuous Endpointbpp_1interim_continuous
Bayesian Predictive Power (BPP) for Time-to-Event Endpointbpp_1interim_t2e
Bayesian Predictive Power (BPP) for Normally Distributed Endpointbpp_2interim
Bayesian Predictive Power (BPP) for Binary Endpointbpp_binary
Bayesian Predictive Power (BPP) for Continuous Endpointbpp_continuous
Bayesian Predictive Power (BPP) for Time-To-Event Endpointbpp_t2e
Posterior density conditional on known interim resultestimate_posterior
Posterior density conditional on interim result is proportional to the value of this functionestimate_posterior_nominator
Product of posterior density and conditional power for known interim resultestimate_toIntegrate
Integrand to compute Bayesian Predictive Power when flat prior has been updated with likelihoodFlatNormalPosterior
Posterior density conditional on interim result, only known as interval, is proportional to the value of this functioninterval_posterior_nominator
Posterior density conditional on two interim results, both only known as intervals, is proportional to the value of this functioninterval_posterior_nominator2
Product of posterior density and conditional power for blinded interim resultinterval_toIntegrate
Product of posterior density and conditional power for blinded interim resultinterval_toIntegrate2
Normal-Normal Posterior in conjugate normal model, for known sigmaNormalNormalPosterior
Conditional power conditioning on a blinded interimpost_power
Density and CDF for Uniform Distribution with Normal tailsdUniformNormalTails pUniformNormalTails