Response-Adaptive Randomization in Clinical Trials

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Documentation for package ‘RARtrials’ version 0.0.1

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