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Robustness of ANCOVA in randomized trials with unequal randomization
Author(s) -
Bartlett Jonathan W.
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13184
Subject(s) - analysis of covariance , covariate , estimator , randomization , statistics , mathematics , robustness (evolution) , econometrics , variance (accounting) , randomized experiment , randomized controlled trial , covariance , repeated measures design , restricted randomization , medicine , business , biochemistry , chemistry , surgery , accounting , gene
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.