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Analysis Of Covariance (Ancova) In Randomized Trials: More Precision, Less Conditional Bias, And Valid Confidence Intervals, Without Model Assumptions, Bingkai Wang, Elizabeth Ogburn, Michael Rosenblum
Analysis Of Covariance (Ancova) In Randomized Trials: More Precision, Less Conditional Bias, And Valid Confidence Intervals, Without Model Assumptions, Bingkai Wang, Elizabeth Ogburn, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
Covariate adjustment" in the randomized trial context refers to an estimator of the average treatment effect that adjusts for chance imbalances between study arms in baseline variables (called “covariates"). The baseline variables could include, e.g., age, sex, disease severity, and biomarkers. According to two surveys of clinical trial reports, there is confusion about the statistical properties of covariate adjustment. We focus on the ANCOVA estimator, which involves fitting a linear model for the outcome given the treatment arm and baseline variables, and trials with equal probability of assignment to treatment and control. We prove the following new (to the best …