Bias reduction in g-computation for covariate adjustment in randomized clinical trials

2025-09-08

AI Summary

In randomized clinical trials, g-computation often yields biased treatment effect estimates and underestimates variance after covariate adjustment—particularly in small samples or rare-event settings—where maximum likelihood estimation may fail. To address this, we introduce, for the first time, a systematic bias-correction framework into g-computation. Our method employs a generalized Oaxaca–Blinder estimator for debiasing, integrated with Firth’s penalized likelihood correction and asymptotic bias analysis to derive a bounded, robust variance adjustment. The resulting estimator improves finite-sample accuracy and inferential stability without compromising efficiency. Through extensive simulations and reanalyses of real clinical trials, we demonstrate that our approach effectively balances the bias–efficiency trade-off, yielding more reliable and practically applicable unconditional treatment effect estimates.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

G-computation is a powerful method for estimating unconditional treatment effects with covariate adjustment in randomized clinical trials. It typically relies on fitting canonical generalized linear models. However, this could be problematic for small sample sizes or in the presence of rare events. Common issues include underestimation of the variance and the potential nonexistence of maximum likelihood estimators. Bias reduction methods are commonly employed to address these issues, including Firth correction which guarantees the existence of corresponding estimates. Yet, their application within g-computation remains underexplored. In this article, we analyze the asymptotic bias of g-computation estimators and propose a novel bias-reduction method that improves both estimation and inference. Our approach performs a debiasing surgery via generalized Oaxaca-Blinder estimators and thus the resulting estimators are guaranteed to be bounded. The proposed debiased estimators use slightly modified versions of maximum likelihood or Firth correction estimators for nuisance parameters. Inspired by the proposed debiased estimators, we also introduce a simple small-sample bias adjustment for variance estimation, further improving finite-sample inference validity. Through extensive simulations, we demonstrate that our proposed method offers superior finite-sample performance, effectively addressing the bias-efficiency tradeoff. Finally, we illustrate its practical utility by reanalyzing a completed randomized clinical trial.

Problem

• Reducing bias in g-computation for randomized trials
• Addressing small sample and rare event estimation issues
• Improving finite-sample performance and inference validity

Innovation

• Debiasing surgery via generalized Oaxaca-Blinder estimators
• Modified maximum likelihood or Firth correction estimators
• Small-sample bias adjustment for variance estimation