Inference Under Covariate-Adaptive Randomization

ABSTRACT

This article studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. Our main requirement is that the randomization scheme assigns treatment status within each stratum so that the fraction of units being assigned to treatment within each stratum has a well behaved distribution centered around a proportion π as the sample size tends to infinity. Such schemes include, for example, Efron’s biased-coin design and stratified block randomization. When testing the null hypothesis that the average treatment effect equals a prespecified value in such settings, we first show the usual two-sample t-test is conservative in the sense that it has limiting rejection probability under the null hypothesis no greater than and typically strictly less than the nominal level. We show, however, that a simple adjustment to the usual standard error of the two-sample t-test leads to a test that is exact in the sense that its limiting rejection probability under the null hypothesis equals the nominal level. Next, we consider the usual t-test (on the coefficient on treatment assignment) in a linear regression of outcomes on treatment assignment and indicators for each of the strata. We show that this test is exact for the important special case of randomization schemes with $\pi =\frac{1}{2}$, but is otherwise conservative. We again provide a simple adjustment to the standard errors that yields an exact test more generally. Finally, we study the behavior of a modified version of a permutation test, which we refer to as the covariate-adaptive permutation test, that only permutes treatment status for units within the same stratum. When applied to the usual two-sample t-statistic, we show that this test is exact for randomization schemes with $\pi =\frac{1}{2}$ and that additionally achieve what we refer to as “strong balance.” For randomization schemes with $\pi \ne \frac{1}{2}$, this test may have limiting rejection probability under the null hypothesis strictly greater than the nominal level. When applied to a suitably adjusted version of the two-sample t-statistic, however, we show that this test is exact for all randomization schemes that achieve “strong balance,” including those with $\pi \ne \frac{1}{2}$. A simulation study confirms the practical relevance of our theoretical results. We conclude with recommendations for empirical practice and an empirical illustration. Supplementary materials for this article are available online.

KEYWORDS:

• Covariate-adaptive randomization
• Efron’s biased-coin design
• Permutation test
• Randomized controlled trial
• Stratified block randomization
• Two-sample t-test

Supplementary Materials

The supplemental material contains the proofs of the main theorems and auxiliary lemmas. It also contains the R files required to replicate the simulations in the article, as well as the data files and R files to replication the empirical application.

Acknowledgments

The authors thank the Co-Editor, the Associate Editor, and the three anonymous referees for useful comments and suggestions and also thank Lori Beaman, Robert Garlick, Raymond Guiteras, Aprajit Mahajan, Joseph Romano, Andres Santos, and seminar participants at various institutions for helpful comments on this article and Yuehao Bai and Winnie van Dijk for excellent research assistance.

Additional information

Funding

The research of the first author was supported by National Institutes of Health Grant 40-4153-00-0-85-399. The research of the second author was supported by National Science Foundation Grant SES-1530534. The research of the third author was supported by National Science Foundation Grants DMS-1308260, SES-1227091, and SES-1530661.