Package: baskexact 1.0.1
baskexact: Analytical Calculation of Basket Trial Operating Characteristics
Analytically calculates the operating characteristics of single-stage and two-stage basket trials with equal sample sizes using the power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988> and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.
Authors:
baskexact_1.0.1.tar.gz
baskexact_1.0.1.tar.gz(r-4.5-noble)baskexact_1.0.1.tar.gz(r-4.4-noble)
baskexact_1.0.1.tgz(r-4.4-emscripten)baskexact_1.0.1.tgz(r-4.3-emscripten)
baskexact.pdf |baskexact.html✨
baskexact/json (API)
# Install 'baskexact' in R: |
install.packages('baskexact', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lbau7/baskexact/issues
Last updated 8 months agofrom:6ce99eafc9. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 06 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 06 2024 |
Exports:adjust_lambdabasket_testcheck_mon_betweencheck_mon_withinecdessestimget_scenariosglobalweights_diffglobalweights_fixinterim_posteriorinterim_postpredopt_designplot_weightspowsetupOneStageBasketsetupTwoStageBaskettoerweights_cppweights_fujikawaweights_jsdweights_mmlweights_poolweights_separate
Dependencies:arrangementsclicodetoolscolorspacedigestdoFutureextraDistrfansifarverforeachfuturefuture.applyggplot2globalsgluegmpgtableisobanditeratorslabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr
Extending baskexact
Rendered fromv2_Extending_baskexact.Rmd
usingknitr::rmarkdown
on Dec 06 2024.Last update: 2024-03-14
Started: 2024-03-14
Introduction to baskexact
Rendered fromv1_Introduction.Rmd
usingknitr::rmarkdown
on Dec 06 2024.Last update: 2024-03-14
Started: 2024-03-14
Reproduce Results From Fujikawa et al. (2020)
Rendered fromv3_Reproduce_Fujikawa.Rmd
usingknitr::rmarkdown
on Dec 06 2024.Last update: 2024-03-14
Started: 2024-03-14
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Adjust Lambda | adjust_lambda adjust_lambda,OneStageBasket-method adjust_lambda,TwoStageBasket-method |
Test for the Results of a Basket Trial | basket_test basket_test,OneStageBasket-method |
Check Between-Trial Monotonicity | check_mon_between check_mon_between,OneStageBasket-method |
Check Within-Trial Monotonicity | check_mon_within check_mon_within,OneStageBasket-method |
Expected number of correct decisions | ecd ecd,OneStageBasket-method ecd,TwoStageBasket-method |
Expected Sample Size | ess ess,TwoStageBasket-method |
Posterior Mean and Mean Squared Error | estim estim,OneStageBasket-method estim,TwoStageBasket-method |
Create a Scenario Matrix | get_scenarios |
Global Weights Based on Response Rate Differences | globalweights_diff |
Fixed Global Weights | globalweights_fix |
Interim analysis based on the posterior probability | interim_posterior interim_posterior,TwoStageBasket-method |
Interim analysis based on the posterior predictive probability | interim_postpred interim_postpred,TwoStageBasket-method |
Class OneStageBasket | OneStageBasket OneStageBasket-class |
Optimize a Basket Design | opt_design opt_design,OneStageBasket-method opt_design,TwoStageBasket-method |
Plot Weight Functions | plot_weights plot_weights,OneStageBasket-method |
Power | pow pow,OneStageBasket-method pow,TwoStageBasket-method |
Setup OneStageBasket | setupOneStageBasket |
Setup TwoStageBasket | setupTwoStageBasket |
Type 1 Error Rate | toer toer,OneStageBasket-method toer,TwoStageBasket-method |
Class TwoStageBasket | TwoStageBasket TwoStageBasket-class |
Weights Based on the Calibrated Power Prior | weights_cpp weights_cpp,OneStageBasket-method weights_cpp,TwoStageBasket-method |
Weights Based on Fujikawa et al.'s Design | weights_fujikawa weights_fujikawa,OneStageBasket-method weights_fujikawa,TwoStageBasket-method |
Weights Based on the Jensen-Shannon Divergence | weights_jsd weights_jsd,OneStageBasket-method weights_jsd,TwoStageBasket-method |
Weights Based on the Marginal Maximum Likelihood | weights_mml weights_mml,OneStageBasket-method weights_mml,TwoStageBasket-method |
Pooled Analysis | weights_pool weights_pool,OneStageBasket-method weights_pool,TwoStageBasket-method |
Separate Analysis in Each Basket | weights_separate weights_separate,OneStageBasket-method weights_separate,TwoStageBasket-method |