​​Covariate adjustment using treatment-blinded covariate selection within randomized clinical trials​
​​When estimating treatment effects in randomized clinical trials, the primary goal of covariate-adjustment is to improve precision (e.g., for linear models) and/or reduce bias (e.g., for non-linear models). In current practice, the covariates for the analysis model are pre-selected. However, knowledge gaps at the trial design stage can unwittingly make covariate pre-selection suboptimal. We propose an alternative approach in which a pre-specified treatment-blinded algorithm is used to identify covariates that are jointly associated with the observed trial outcome(s) of interest, followed by a corresponding covariate-adjusted analysis for treatment effect estimation and inference. The utility of our proposal is illustrated using real data examples and simulations.
Speaker
​​Devan V. Mehrotra
​​Devan V. Mehrotra, PhD, is Vice President of Biostatistics and Research Decision Sciences at Merck Research Laboratories (MRL), the R&D Division of Merck & Co., Inc. in the US. ​Over the past 33 years, he has made significant contributions towards the research, development and regulatory approval of medical drugs and vaccines across a broad spectrum of therapeutic areas. He was awarded an MRL Presidential Fellowship in 2012. Devan is also an adjunct Professor of Biostatistics at the University of Pennsylvania and an elected Fellow of the American Statistical Association. He has served as a subject matter expert for the Bill and Melinda Gates Foundation (for HIV vaccine development), the US National Academy of Sciences (for missing data issues in clinical trials), the Coalition for Epidemic Preparedness Innovations (for COVID-19 vaccine development), and the International Council on Harmonization (for ICH E9/R1 on estimands and sensitivity analyses).
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