Regression-assisted inference for the average treatment effect in paired experiments

In paired randomized experiments, individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use of regression adjustment to correct for persistent covariate imbalances after randomization, and present two regressio...

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Bibliographic Details
Published in:Biometrika 2018-12, Vol.105 (4), p.994-1000
Main Author: FOGARTY, COLIN B.
Format: Article
Language:English
Subjects:
Asymptotic methods
Asymptotic properties
Computer simulation
Confidence intervals
Estimators
Experiments
Inference
Miscellanea
Randomization
Regression analysis
Regression models
Statistical analysis
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Summary:In paired randomized experiments, individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use of regression adjustment to correct for persistent covariate imbalances after randomization, and present two regression-assisted estimators for the sample average treatment effect in paired experiments. Using the potential outcomes framework, we prove that these estimators are consistent for the sample average treatment effect under mild regularity conditions even if the regression model is improperly specified, and describe how asymptotically conservative confidence intervals can be constructed. We demonstrate that the variances of the regression-assisted estimators are no larger than that of the standard difference-in-means estimator asymptotically, and illustrate the proposed methods by simulation. The analysis does not require a superpopulation model, a constant treatment effect, or the truth of the regression model, and hence provides inference for the sample average treatment effect with the potential to increase power without unrealistic assumptions.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asy034