Covariate-Adaptive Randomization for Clinical Trials
Covariate-adjusted Biased Coin Design
Covariate-adjusted Biased Coin Design with Covariate Data Generating M...
Command-line User Interface Using Covariate-adjusted Biased Coin Desig...
Bootstrap t-test
carat-package: Covariate-Adaptive Randomization for Clinical Trials
Comparison of Powers for Different Tests under Different Randomization...
Compare Different Randomization Procedures via Tables and Plots
Corrected t-test
Atkinson's -optimal Biased Coin Design
Atkinson's -optimal Biased Coin Design with Covariate Data Genera...
Command-line User Interface Using Atkinson's -optimal Biased Coin...
Evaluation of Tests and Randomization Procedures through Power
Evaluation of Randomization Procedures
Evaluation Randomization Procedures with Covariate Data Generating Mec...
Data Generation
Hu and Hu's General Covariate-Adaptive Randomization
Hu and Hu's General Covariate-Adaptive Randomization with Covariate Da...
Command-line User Interface Using Hu and Hu's General Covariate-adapti...
Pocock and Simon's Method in the Two-Arms Case
Pocock and Simon's Method in the Two-Arms Case with Covariate Data Gen...
Command-line User Interface Using Pocock and Simon's Procedure with Tw...
Randomization Test
Shao's Method in the Two-Arms Case
Shao's Method in the Two-Arms Case with Covariate Data Generating Mech...
Command-line User Interface Using Shao's Method
Stratified Permuted Block Randomization
Stratified Permuted Block Randomization with Covariate Data Generating...
Command-line User Interface Using Stratified Permuted Block Randomizat...
Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.