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Discussion on “Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment for binary, ordinal, and time‐to‐event outcomes”

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  • Michael A. Proschan

Abstract

Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time‐to‐event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID‐19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization‐based inference.

Suggested Citation

  • Michael A. Proschan, 2021. "Discussion on “Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment for binary, ordinal, and time‐to‐event outcomes”," Biometrics, The International Biometric Society, vol. 77(4), pages 1482-1484, December.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:4:p:1482-1484
    DOI: 10.1111/biom.13493
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    References listed on IDEAS

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    1. David Benkeser & Iván Díaz & Alex Luedtke & Jodi Segal & Daniel Scharfstein & Michael Rosenblum, 2021. "Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1467-1481, December.
    2. Simon, Richard & Simon, Noah Robin, 2011. "Using randomization tests to preserve type I error with response adaptive and covariate adaptive randomization," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 767-772, July.
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