Package: RARtrials 0.0.1

RARtrials: Response-Adaptive Randomization in Clinical Trials

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

Authors:Chuyao Xu [aut, cre], Thomas Lumley [aut, ths], Alain Vandal [aut, ths], Sofia S. Villar [cph], Kyle J. Wathen [cph]

RARtrials_0.0.1.tar.gz
RARtrials_0.0.1.zip(r-4.7-any)RARtrials_0.0.1.zip(r-4.6-any)RARtrials_0.0.1.zip(r-4.5-any)
RARtrials_0.0.1.tgz(r-4.6-any)RARtrials_0.0.1.tgz(r-4.5-any)
RARtrials_0.0.1.tar.gz(r-4.7-any)RARtrials_0.0.1.tar.gz(r-4.6-any)
RARtrials_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RARtrials/json (API)

# Install 'RARtrials' in R:

install.packages('RARtrials', repos = c('https://yayayaoyaoyao.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yayayaoyaoyao/rartrials/issues

On CRAN:

3.70 score 1 scripts 234 downloads 33 exports 29 dependencies

Last updated from:0aaf5bad2a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	134
source / vignettes	OK	200
linux-release	OK	141
macos-release	OK	102
macos-oldrel	OK	141
windows-devel	OK	96
windows-release	OK	105
windows-oldrel	OK	102
wasm-release	OK	101

Exports:brar_select_au_binary brar_select_au_known_var brar_select_au_unknown_var convert_chisq_to_gamma convert_gamma_to_chisq dabcd_max_power dabcd_min_var flgi_cut_off_binary flgi_cut_off_known_var flgi_cut_off_unknown_var Gittins pgreater_beta pgreater_NIX pgreater_normal pmax_beta pmax_NIX pmax_normal sim_A_optimal_known_var sim_A_optimal_unknown_var sim_Aa_optimal_known_var sim_Aa_optimal_unknown_var sim_brar_binary sim_brar_known_var sim_brar_unknown_var sim_dabcd_max_power sim_dabcd_min_var sim_flgi_binary sim_flgi_known_var sim_flgi_unknown_var sim_RPTW sim_RSIHR_optimal_known_var sim_RSIHR_optimal_unknown_var update_par_nichisq

Dependencies:askpass cli curl digest fs generics glue httr jsonlite lifecycle magrittr mime openssl pillar pins pkgconfig purrr R6 rappdirs rbibutils Rdpack rlang sys tibble utf8 vctrs whisker withr yaml

Response-Adaptive Randomization in Clinical Trials

The Randomized Play-the-Winner Rule for Binary Outcomes | The doubly Adaptive Biased Coin Design for Binary Outcomes | $A$-optimal Allocation, $A_a$-optimal Allocation and Generalized RSIHR Allocation for Continuous Outcomes | Bayesian Response-Adaptive Randomisation with a Control Group | R code | Forward-Looking Gittins Index | References

Rendered fromRARtrials.Rmdusingknitr::rmarkdown

Last update: 2026-02-03
Started: 2024-04-28

Help page	Topics
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Binary Endpoint	brar_select_au_binary
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variances	brar_select_au_known_var
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variances	brar_select_au_unknown_var
Convert parameters from a Normal-Inverse-Chi-Squared Distribution to a Normal-Inverse-Gamma Distribution	convert_chisq_to_gamma
Convert parameters from a Normal-Inverse-Gamma Distribution to a Normal-Inverse-Chi-Squared Distribution	convert_gamma_to_chisq
Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint	dabcd_max_power
Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Minimal Variance Strategy for Binary Endpoint	dabcd_min_var
Cut-off Value of the Forward-looking Gittins Index Rule in Binary Endpoint	flgi_cut_off_binary
Cut-off Value of the Forward-looking Gittins Index Rule in Continuous Endpoint with Known Variances	flgi_cut_off_known_var
Cut-off Value of the Forward-looking Gittins Index rule in Continuous Endpoint with Unknown Variances	flgi_cut_off_unknown_var
Gittins Indices	Gittins
Calculate the Futility Stopping Probability for Binary Endpoint with Beta Distribution	pgreater_beta
Calculate the Futility Stopping Probability for Continuous Endpoint with Unknown Variances Using a Normal-Inverse-Chi-Squared Distribution	pgreater_NIX
Calculate the Futility Stopping Probability for Continuous Endpoint with Known Variances Using Normal Distribution	pgreater_normal
Posterior Probability that a Particular Arm is the Best for Binary Endpoint	pmax_beta
Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Unknown Variances	pmax_NIX
Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Known Variances	pmax_normal
Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Known Variances	sim_A_optimal_known_var
Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Unknown Variances	sim_A_optimal_unknown_var
Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Known Variances	sim_Aa_optimal_known_var
Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Unknown Variances	sim_Aa_optimal_unknown_var
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Binary Outcomes	sim_brar_binary
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variances	sim_brar_known_var
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variances	sim_brar_unknown_var
Simulate a Trial Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint	sim_dabcd_max_power
Simulate a Trial Using Doubly Adaptive Biased Coin Design with Minmial Variance Strategy for Binary Endpoint	sim_dabcd_min_var
Simulate a Trial Using Forward-Looking Gittins Index for Binary Endpoint	sim_flgi_binary
Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Known Variances	sim_flgi_known_var
Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Unknown Variances	sim_flgi_unknown_var
Simulate a Trial Using Randomized Play-the-Winner Rule for Binary Endpoint	sim_RPTW
Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Known Variances	sim_RSIHR_optimal_known_var
Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Unknown Variances	sim_RSIHR_optimal_unknown_var
Update Parameters of a Normal-Inverse-Chi-Squared Distribution with Available Data	update_par_nichisq

Package: RARtrials 0.0.1

RARtrials: Response-Adaptive Randomization in Clinical Trials

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