Response Adaptive Randomization with 'Frequentist' Approaches
Comparison of Powers for Treatment Effects under Different SEU Randomi...
Sequential Estimation-adjusted Urn Model with Simulated Data (Binary D...
Sequential Estimation-adjusted Urn Model with Simulated Data (Gaussian...
Doubly Adaptive Biased Coin Design (Binary Responses)
Doubly Adaptive Biased Coin Design (Binary Data Frame)
Doubly Adaptive Biased Coin Design (Gaussian Responses)
Doubly Adaptive Biased Coin Design (Gaussian Responses)
Comparison of Powers for Different Tests under Different DBCD Randomiz...
Comparison of Powers for Different Tests under Different DBCD Randomiz...
Comparison of Powers for Treatment Effects under Different DBCD Random...
Comparison of Powers for Treatment Effects under Different DBCD Random...
Sequential Estimation-adjusted Urn Model (Binary Data)
Sequential Estimation-adjusted Urn Model (Gaussian Responses)
Comparison of Powers for Sample Sizes under Different SEU Randomizatio...
Comparison of Powers for Sample Sizes under Different SEU Randomizatio...
Comparison of Powers for Treatment Effects under Different SEU Randomi...
Doubly Adaptive Biased Coin Design with Simulated Data (Binary Respons...
Doubly Adaptive Biased Coin Design with Simulated Data (Gaussian Respo...
Provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or 'ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. 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. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for 'DBCD' and six addition rule functions for 'SEU' are implemented to target allocations such as 'Neyman', 'Rosenberger' Rosenberger et al. (2001) <doi:10.1111/j.0006-341X.2001.00909.x> and 'Urn' allocations.