Package: bhmbasket 0.9.5

bhmbasket: Bayesian Hierarchical Models for Basket Trials

Provides functions for the evaluation of basket trial designs with binary endpoints. Operating characteristics of a basket trial design are assessed by simulating trial data according to scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and assessing decision probabilities on stratum and trial-level based on Go / No-go decision making. The package is build for high flexibility regarding decision rules, number of interim analyses, number of strata, and recruitment. The BHMs proposed by Berry et al. (2013) <doi:10.1177/1740774513497539> and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>, as well as a model that combines both approaches are implemented. Functions are provided to implement Bayesian decision rules as for example proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>. In addition, posterior point estimates (mean/median) and credible intervals for response rates and some model parameters can be calculated. For simulated trial data, bias and mean squared errors of posterior point estimates for response rates can be provided.

Authors:Stephan Wojciekowski [aut, cre]

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bhmbasket/json (API)
NEWS

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

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

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

2.78 score 1 stars 1 packages 5 scripts 283 downloads 23 exports 21 dependencies

Last updated 3 years agofrom:3e96ad7d87. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winOKNov 01 2024
R-4.5-linuxOKNov 01 2024
R-4.4-winOKNov 01 2024
R-4.4-macOKNov 01 2024
R-4.3-winOKNov 01 2024
R-4.3-macOKNov 01 2024

Exports:combinePriorParameterscontinueRecruitmentcreateTrialgetEstimatesgetGoDecisionsgetGoProbabilitiesgetMuVargetPriorParametersinvLogitloadAnalysesloadScenarioslogitnegateGoDecisionsperformAnalysessaveAnalysessaveScenariosscaleRoundListsetPriorParametersBerrysetPriorParametersExNexsetPriorParametersExNexAdjsetPriorParametersPooledsetPriorParametersStratifiedsimulateScenarios

Dependencies:abindbootclicodacodetoolsdigestdoRNGforeachglueiteratorslatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangrngtoolsstringistringrvctrs

Reproducing Parts of Neuenschwander et al. (2016)

Rendered fromreproduceExNex.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2022-01-18
Started: 2021-02-15

Running bhmbasket on HPC

Rendered fromRunning_bhmbasket_on_HPC.Rmdusingknitr::rmarkdownon Nov 01 2024.

Last update: 2022-02-14
Started: 2022-01-18

Readme and manuals

Help Manual

Help pageTopics
combinePriorParameterscombinePriorParameters
continueRecruitmentcontinueRecruitment
createTrialcreateTrial
getEstimatesgetEstimates
getGoDecisionsgetGoDecisions
getGoProbabilitiesgetGoProbabilities
getMuVargetMuVar
getPriorParametersgetPriorParameters
invLogitinvLogit
loadAnalysesloadAnalyses
loadScenariosloadScenarios
logitlogit
negateGoDecisionsnegateGoDecisions
performAnalysesperformAnalyses
saveAnalysessaveAnalyses
saveScenariossaveScenarios
scaleRoundListscaleRoundList
setPriorParametersBerrysetPriorParametersBerry
setPriorParametersExNexsetPriorParametersExNex
setPriorParametersExNexAdjsetPriorParametersExNexAdj
setPriorParametersPooledsetPriorParametersPooled
setPriorParametersStratifiedsetPriorParametersStratified
simulateScenariossimulateScenarios