RARfreq0.1.5 package

Response Adaptive Randomization with 'Frequentist' Approaches

SEU_power_comparison_Power_vs_Trt_GAUSSIAN

Comparison of Powers for Treatment Effects under Different SEU Randomi...

SEU_simulation_main

Sequential Estimation-adjusted Urn Model with Simulated Data (Binary D...

SEU_simulation_main_GAUSSIAN

Sequential Estimation-adjusted Urn Model with Simulated Data (Gaussian...

DBCD_BINARY

Doubly Adaptive Biased Coin Design (Binary Responses)

DBCD_BINARY_raw

Doubly Adaptive Biased Coin Design (Binary Data Frame)

DBCD_GAUSSIAN

Doubly Adaptive Biased Coin Design (Gaussian Responses)

DBCD_GAUSSIAN_raw

Doubly Adaptive Biased Coin Design (Gaussian Responses)

power_comparison_Power_vs_n

Comparison of Powers for Different Tests under Different DBCD Randomiz...

power_comparison_Power_vs_n_GAUSSIAN

Comparison of Powers for Different Tests under Different DBCD Randomiz...

power_comparison_Power_vs_Trt

Comparison of Powers for Treatment Effects under Different DBCD Random...

power_comparison_Power_vs_Trt_GAUSSIAN

Comparison of Powers for Treatment Effects under Different DBCD Random...

SEU_BINARY_raw

Sequential Estimation-adjusted Urn Model (Binary Data)

SEU_GAUSSIAN_raw

Sequential Estimation-adjusted Urn Model (Gaussian Responses)

SEU_power_comparison_Power_vs_n

Comparison of Powers for Sample Sizes under Different SEU Randomizatio...

SEU_power_comparison_Power_vs_n_GAUSSIAN

Comparison of Powers for Sample Sizes under Different SEU Randomizatio...

SEU_power_comparison_Power_vs_Trt

Comparison of Powers for Treatment Effects under Different SEU Randomi...

simulation_main

Doubly Adaptive Biased Coin Design with Simulated Data (Binary Respons...

simulation_main_GAUSSIAN

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.

  • Maintainer: Xiu Huang
  • License: MIT + file LICENSE
  • Last published: 2024-05-07