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
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RARtrials.pdf |RARtrials.html✨
RARtrials/json (API)

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

Peer review:

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

On CRAN:

33 exports 1.25 score 31 dependencies 87 downloads

Last updated 4 months agofrom:7b2b2e28b2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-winOKSep 03 2024
R-4.5-linuxOKSep 03 2024
R-4.4-winOKSep 03 2024
R-4.4-macOKSep 03 2024
R-4.3-winOKSep 03 2024
R-4.3-macOKSep 03 2024

Exports:brar_select_au_binarybrar_select_au_known_varbrar_select_au_unknown_varconvert_chisq_to_gammaconvert_gamma_to_chisqdabcd_max_powerdabcd_min_varflgi_cut_off_binaryflgi_cut_off_known_varflgi_cut_off_unknown_varGittinspgreater_betapgreater_NIXpgreater_normalpmax_betapmax_NIXpmax_normalsim_A_optimal_known_varsim_A_optimal_unknown_varsim_Aa_optimal_known_varsim_Aa_optimal_unknown_varsim_brar_binarysim_brar_known_varsim_brar_unknown_varsim_dabcd_max_powersim_dabcd_min_varsim_flgi_binarysim_flgi_known_varsim_flgi_unknown_varsim_RPTWsim_RSIHR_optimal_known_varsim_RSIHR_optimal_unknown_varupdate_par_nichisq

Dependencies:askpassclicurldigestellipsisfansifsgenericsgluehttrjsonlitelifecyclemagrittrmimeopensslpillarpinspkgconfigpurrrR6rappdirsrbibutilsRdpackrlangsystibbleutf8vctrswhiskerwithryaml

Response-Adaptive Randomization in Clinical Trials

Chuyao Xu, Thomas Lumley, Alain Vandal

Rendered fromRARtrials.Rmdusingknitr::rmarkdownon Sep 03 2024.

Last update: 2024-06-02
Started: 2024-04-28

Readme and manuals

Help Manual

Help pageTopics
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Binary Endpointbrar_select_au_binary
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variancesbrar_select_au_known_var
Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variancesbrar_select_au_unknown_var
Convert parameters from a Normal-Inverse-Chi-Squared Distribution to a Normal-Inverse-Gamma Distributionconvert_chisq_to_gamma
Convert parameters from a Normal-Inverse-Gamma Distribution to a Normal-Inverse-Chi-Squared Distributionconvert_gamma_to_chisq
Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpointdabcd_max_power
Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Minimal Variance Strategy for Binary Endpointdabcd_min_var
Cut-off Value of the Forward-looking Gittins Index Rule in Binary Endpointflgi_cut_off_binary
Cut-off Value of the Forward-looking Gittins Index Rule in Continuous Endpoint with Known Variancesflgi_cut_off_known_var
Cut-off Value of the Forward-looking Gittins Index rule in Continuous Endpoint with Unknown Variancesflgi_cut_off_unknown_var
Gittins IndicesGittins
Calculate the Futility Stopping Probability for Binary Endpoint with Beta Distributionpgreater_beta
Calculate the Futility Stopping Probability for Continuous Endpoint with Unknown Variances Using a Normal-Inverse-Chi-Squared Distributionpgreater_NIX
Calculate the Futility Stopping Probability for Continuous Endpoint with Known Variances Using Normal Distributionpgreater_normal
Posterior Probability that a Particular Arm is the Best for Binary Endpointpmax_beta
Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Unknown Variancespmax_NIX
Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Known Variancespmax_normal
Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Known Variancessim_A_optimal_known_var
Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Unknown Variancessim_A_optimal_unknown_var
Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Known Variancessim_Aa_optimal_known_var
Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Unknown Variancessim_Aa_optimal_unknown_var
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Binary Outcomessim_brar_binary
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variancessim_brar_known_var
Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variancessim_brar_unknown_var
Simulate a Trial Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpointsim_dabcd_max_power
Simulate a Trial Using Doubly Adaptive Biased Coin Design with Minmial Variance Strategy for Binary Endpointsim_dabcd_min_var
Simulate a Trial Using Forward-Looking Gittins Index for Binary Endpointsim_flgi_binary
Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Known Variancessim_flgi_known_var
Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Unknown Variancessim_flgi_unknown_var
Simulate a Trial Using Randomized Play-the-Winner Rule for Binary Endpointsim_RPTW
Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Known Variancessim_RSIHR_optimal_known_var
Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Unknown Variancessim_RSIHR_optimal_unknown_var
Update Parameters of a Normal-Inverse-Chi-Squared Distribution with Available Dataupdate_par_nichisq