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Robustness of ANCOVA in randomized trials with unequal randomization

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  • Jonathan W. Bartlett

Abstract

Randomized trials with continuous outcomes are often analyzed using analysis of covariance (ANCOVA), with adjustment for prognostic baseline covariates. The ANCOVA estimator of the treatment effect is consistent under arbitrary model misspecification. In an article recently published in the journal, Wang et al proved the model‐based variance estimator for the treatment effect is also consistent under outcome model misspecification, assuming the probability of randomization to each treatment is 1/2. In this reader reaction, we derive explicit expressions which show that when randomization is unequal, the model‐based variance estimator can be biased upwards or downwards. In contrast, robust sandwich variance estimators can provide asymptotically valid inferences under arbitrary misspecification, even when randomization probabilities are not equal.

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  • Jonathan W. Bartlett, 2020. "Robustness of ANCOVA in randomized trials with unequal randomization," Biometrics, The International Biometric Society, vol. 76(3), pages 1036-1038, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:1036-1038
    DOI: 10.1111/biom.13184
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    References listed on IDEAS

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    1. Yang L. & Tsiatis A. A., 2001. "Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial," The American Statistician, American Statistical Association, vol. 55, pages 314-321, November.
    2. Bingkai Wang & Elizabeth L. Ogburn & Michael Rosenblum, 2019. "Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions," Biometrics, The International Biometric Society, vol. 75(4), pages 1391-1400, December.
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