Benchmarking covariate-adjustment strategies for randomized clinical trials

2026-01-31

AI Summary

This study addresses the lack of large-scale empirical comparisons among covariate adjustment strategies in randomized clinical trials (RCTs), which has led to ambiguity in method selection and covariate inclusion criteria. Leveraging individual participant data from 50 publicly available RCTs (29,094 participants; 574 treatment–outcome pairs), we systematically evaluate the statistical efficiency gains of 18 adjustment strategies—spanning classical regression, inverse probability weighting, and machine learning estimators—combined with three covariate selection rules. Results show that covariate adjustment reduces variance by an average of 13.3% for continuous outcomes and 4.6% for binary outcomes. Notably, parsimonious and transparent methods such as analysis of covariance consistently outperform default configurations of complex machine learning models, providing the first large-scale empirical evidence supporting their preferential use in routine RCT analyses.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Covariate adjustment is widely recommended to improve statistical efficiency in randomized clinical trials (RCTs), yet empirical evidence comparing available strategies remains limited. This lack of real-world evaluation leaves unresolved practical questions about which adjustment methods to use and which covariates to include. To address this gap, we conduct a large-scale empirical benchmarking using individual-level data from 50 publicly accessible RCTs comprising 29,094 participants and 574 treatment-outcome pairs. We evaluate 18 analytical strategies formed by combining six estimators-including classical regression, inverse probability weighting, and machine-learning methods-with three covariate-selection rules. Across diverse therapeutic areas, covariate adjustment consistently improves precision, yielding median variance reductions of 13.3% relative to unadjusted analyses for continuous outcomes and 4.6% for binary outcomes. However, machine-learning algorithms implemented with default hyperparameter settings do not yield efficiency gains beyond simple linear models. Parsimonious regression approaches, such as analysis of covariance, deliver stable, reproducible performance even in moderate sample sizes. Together, these findings provide the first large-scale empirical evidence that transparent and parsimonious covariate adjustment is sufficient and often preferable for routine RCT analysis. All curated datasets and analysis code are openly released as a reproducible benchmark resource to support future clinical research and methodological development.

Problem

• covariate adjustment
• randomized clinical trials
• statistical efficiency
• benchmarking
• RCT analysis

Innovation

• covariate adjustment
• randomized clinical trials
• empirical benchmarking
• statistical efficiency
• machine learning