Robust variance estimation for covariate-adjusted unconditional treatment effect in randomized clinical trials with binary outcomes

• Published in: Statistical theory and related fields Vol. 7; no. 2; pp. 159 - 163
• Main Authors: Ye, Ting, Bannick, Marlena, Yi, Yanyao, Shao, Jun
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2023; Taylor & Francis Group
• Subjects: G-computation; logistic regression; model-assisted; nonlinear covariate adjustment; risk difference; standardization; G-computation; Risk difference; Logistic regression; Nonlinear covariate adjustment; Model-assisted; Standardization
• ISSN: 2475-4269; 2475-4277; 2475-4277
• DOI: 10.1080/24754269.2023.2205802

To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.