Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches

Laura B. Balzer; Lei Nie; Issa J. Dahabreh; Kajsa Kvist; Tianyue Zhou; Demissie Alemayehu; Larry Han; Zhiwei Zhang; Salina P. Waddy; Andrew Mertens; Christian B. Pipper; Ken Wiley, Jr.; Margot Yann; Gilmer Valdes; Xu Shi; Mark van der Laan; Maya Petersen; Kelly Van Lancker

arXiv · 2607.27542

Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches

Laura B. Balzer·Lei Nie·Issa J. Dahabreh·Kajsa Kvist·Tianyue Zhou·Demissie Alemayehu·Larry Han·Zhiwei Zhang·Salina P. Waddy·Andrew Mertens·Christian B. Pipper·Ken Wiley, Jr.·Margot Yann·Gilmer Valdes·Xu Shi·Mark van der Laan·Maya Petersen·Kelly Van Lancker

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Abstract

While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for baseline variables that are prognostic of the outcome can reduce estimator variance, resulting in narrower confidence intervals and increased statistical power. Recent guidance by the U.S. Food and Drug Administration supports fixed adjustment for prognostic covariates using parametric regression models. However, this guidance does not address more flexible approaches using data-adaptive or machine learning methods. We offer our perspectives on covariate adjustment to improve analytic precision. We focus on estimating the average effect for the target population in trials with minimal outcome missingness. We provide a non-technical overview of effect estimators that are unadjusted and effect estimators using fixed versus data-adaptive adjustment. We offer practical suggestions for conducting adjusted analyses that are data-adaptive, fully pre-specified, transparently and reproducibly implemented, robust to model misspecification, and guaranteed to improve precision relative to unadjusted analyses --- all while preserving statistical validity and the causal effect of interest. We hope that sharing our perspectives will foster broader discussion and eventual acceptance of principled, pre-specified, data-adaptive covariate adjustment in randomized trials.

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Laura B. Balzer, Lei Nie, Issa J. Dahabreh, Kajsa Kvist, Tianyue Zhou, Demissie Alemayehu, Larry Han, Zhiwei Zhang, Salina P. Waddy, Andrew Mertens, Christian B. Pipper, Ken Wiley, Jr., Margot Yann, Gilmer Valdes, Xu Shi, Mark van der Laan, Maya Petersen, Kelly Van Lancker. 2026-07-30. Towards Best Practices for Covariate Adjustment in Regulatory Trials: From Fixed to Data-Adaptive Approaches. https://arxiv.org/abs/2607.27542

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