Package: OptimalGoldstandardDesigns 1.0.1

OptimalGoldstandardDesigns: Design Parameter Optimization for Gold-Standard Non-Inferiority Trials

Methods to calculate optimal design parameters for one- and two-stage three-arm group-sequential gold-standard non-inferiority trial designs with or without binding or nonbinding futility boundaries, as described in Meis et al. (2023) <doi:10.1002/sim.9630>.

Authors:Jan Meis [aut, cre]

OptimalGoldstandardDesigns_1.0.1.tar.gz
OptimalGoldstandardDesigns_1.0.1.zip(r-4.5)OptimalGoldstandardDesigns_1.0.1.zip(r-4.4)OptimalGoldstandardDesigns_1.0.1.zip(r-4.3)
OptimalGoldstandardDesigns_1.0.1.tgz(r-4.4-any)OptimalGoldstandardDesigns_1.0.1.tgz(r-4.3-any)
OptimalGoldstandardDesigns_1.0.1.tar.gz(r-4.5-noble)OptimalGoldstandardDesigns_1.0.1.tar.gz(r-4.4-noble)
OptimalGoldstandardDesigns_1.0.1.tgz(r-4.4-emscripten)OptimalGoldstandardDesigns_1.0.1.tgz(r-4.3-emscripten)
OptimalGoldstandardDesigns.pdf |OptimalGoldstandardDesigns.html✨
OptimalGoldstandardDesigns/json (API)

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

Peer review:

Bug tracker:https://github.com/jan-imbi/optimalgoldstandarddesigns/issues

On CRAN:

designsgold-standardnon-inferiorityoptimalthree-arm

3.70 score 5 scripts 199 downloads 2 exports 20 dependencies

Last updated 1 years agofrom:45a429112c. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-winNOTENov 19 2024
R-4.5-linuxNOTENov 19 2024
R-4.4-winNOTENov 19 2024
R-4.4-macNOTENov 19 2024
R-4.3-winOKNov 19 2024
R-4.3-macOKNov 19 2024

Exports:optimize_design_onestageoptimize_design_twostage

Dependencies:clidplyrfansigenericsgluelifecyclemagrittrmvtnormnloptrpillarpkgconfigR6rbibutilsRdpackrlangtibbletidyselectutf8vctrswithr

Usage guidance

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Nov 19 2024.

Last update: 2023-09-11
Started: 2022-05-16

Readme and manuals

Help Manual

Help pageTopics
Helper function to calculate the average sample sizecalc_ASN
Calculate the average placebo group sample sizecalc_ASNP
Helper function to calculate allocation ratios from stagewise sample sizescalc_c
Calculate the (local) conditional type I errors of both hypothesis given both interim test statistics.calc_conditional_local_rejection_probs
Calculate the conditional power to reject both hypothesis given both interim test statistics.calc_conditional_power
Helper function to calculate "cumulative allocation ratio" from stagewise allocation ratioscalc_cumc
Helper function to calculate cumulative sample sizes from stagewise sample sizescalc_cumn
Helper function to calculate the final state probabilitiescalc_final_state_probs
Helper function to calculate gamma factors from group variances and cumulative allocation ratioscalc_gamma
Helper function to calculate the local type I error rates of a Designcalc_local_alphas
Helper function to calculate the local rejection boundaries of group sequential testing procedure associated with the hypothesis belong to the groups argumentcalc_local_rejection_boundaries
Helper function to calculate expected value of normal test statistic vector c(Z_TP1, Z_TP2, Z_TC1, Z_TP2) under the null and alternative hypothesis given nT1, gamma and mu.calc_mu_vec
Helper function to calculate expected value of normal test statistic vector c(Z_TP1, Z_TP2, Z_TC1, Z_TP2) under the null and alternative hypothesis given nT1=1, gamma and mu.calc_mu_wo_nT1
Helper function to calculate other n's given n_1,T and allocation ratioscalc_n_from_c
Helper function to calculate the required sample size (of the stage 1 treatment group) to achieve the target power given the bTC2ecalc_nT1_wrt_bTC2e
Helper function to calculate the probability to reject both hypotheses given the mean of the normal test statistic vector c(Z_TP1, Z_TP2, Z_TC1, Z_TC2).calc_prob_reject_both
Helper function to calculate the probability to reject both hypotheses given the mean of the normal test statistic vector c(Z_TP1, Z_TC1).calc_prob_reject_both_singlestage
Helper function to calculate the covariance matrix from the group variances, cumulative allocation ratios and gamma factorscalc_Sigma
Helper function to calculate the maximal probability of rejecting the non-inferiority hypothesis in the testing procedure featuring nonsequential futility, given a point hypothesis for the superiority hypothesis.calc_worst_type_I_error
Calculate the conditional mean of a multivariate normal distributionconditional_mean
Calculate the conditional mean of a multivariate normal distributionconditional_Sigma
Objective function for single-stage gold-standard designsobjective_onestage
Objective function for two-stage gold-standard designsobjective_twostage
OptimalGoldstandardDesignsOptimalGoldstandardDesigns-package OptimalGoldstandardDesigns
Calculate optimal design parameters for a single-stage gold-standard designoptimize_design_onestage
Calculate optimal design parameters for a two-stage gold-standard designoptimize_design_twostage
Add whitespace padding to stringpadd_whitespace
mvtnorm::pmvnorm, but returns 0 if any lower boundary is larger than any upper boundarypmvnorm_
mvtnorm::pmvt, but returns 0 if any lower boundary is larger than any upper boundarypmvt_
Printing method for optimal single-stage goldstandard designsprint.OneStageGoldStandardDesign
Printing method for optimal two-stage goldstandard designsprint.TwoStageGoldStandardDesign