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Balanced covariates with response adaptive randomization.

Benjamin R Saville1,2, Scott M Berry1,3

  • 1Berry Consultants, Austin, TX, 78746, USA.

Pharmaceutical Statistics
|March 7, 2017
PubMed
Summary
This summary is machine-generated.

Response adaptive randomization (RAR) in clinical trials can lead to covariate imbalance. Our new method modifies RAR probabilities to ensure balanced covariate distribution across treatment arms, improving trial result interpretation.

Keywords:
Bayesian RAR, clinical trial, covariate balance, imbalance, response adaptive randomization, stratification

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Response adaptive randomization (RAR) is used in clinical trials to adjust treatment allocation based on accumulating data.
  • RAR methods can inadvertently lead to imbalances in the distribution of important covariates across treatment arms.
  • Covariate imbalance complicates the interpretation of trial results, potentially confounding treatment effects with covariate effects.

Purpose of the Study:

  • To propose a novel method for balancing covariate distribution within response adaptive randomization clinical trials.
  • To ensure that observed treatment differences are attributable to the interventions rather than confounding covariates.

Main Methods:

  • The proposed method modifies global RAR probabilities to generate stratum-specific probabilities.
  • Odds ratios are utilized to adjust these probabilities based on covariate strata.
  • The strategy is designed to be applicable to any RAR method and any type of clinical outcome.

Main Results:

  • Illustrative examples and a simulation study demonstrate the effectiveness of the proposed strategy.
  • The method successfully maintains covariate balance across treatment arms in RAR settings.
  • The approach is shown to be straightforward to implement.

Conclusions:

  • The developed method effectively addresses the challenge of covariate imbalance in response adaptive randomization.
  • This strategy enhances the reliability and interpretability of clinical trial findings derived from RAR.
  • The method offers a practical solution for improving the design and analysis of adaptive clinical trials.