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Covariate adjustment in Bayesian adaptive randomized controlled trials.

James Willard1, Shirin Golchi1, Erica Em Moodie1

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Summary
This summary is machine-generated.

Covariate adjustment in Bayesian adaptive trials boosts statistical power and the likelihood of early stopping for superior treatments. This method also reduces the overall sample size needed for definitive results.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Covariate adjustment enhances power in conventional randomized controlled trials.
  • Flexible frequentist designs benefit from covariate adjustment.
  • Bayesian adaptive designs, popular for their flexibility, lack characterized covariate adjustment.

Purpose of the Study:

  • To characterize covariate adjustment within Bayesian adaptive designs, specifically those allowing early stopping for superiority.
  • To evaluate the impact of covariate adjustment on trial power, early stopping probability, and sample size.

Main Methods:

  • Focused on Bayesian adaptive designs with interim analyses for early stopping.
  • Considered both collapsible and non-collapsible estimands.
  • Conducted simulation studies with various adjustment models and a real-world COVID-19 trial application.

Main Results:

  • Covariate adjustment consistently increased statistical power across all simulated scenarios.
  • The probability of stopping trials early due to treatment superiority was enhanced by covariate adjustment.
  • Expected sample sizes were reduced when employing covariate adjustment compared to unadjusted analyses.

Conclusions:

  • Covariate adjustment is beneficial in Bayesian adaptive trials, improving efficiency and decision-making.
  • The findings support the integration of covariate adjustment into Bayesian adaptive trial designs.
  • This approach offers advantages for optimizing clinical trial resource allocation and speed.