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

作     者：Ting Ye Marlena Bannick Yanyao Yi Jun Shao Ting Ye;Marlena Bannick;Yanyao Yi;Jun Shao

作者机构：Department of BiostatisticsUniversity of WashingtonSeattleWAUSA Global Statistical SciencesEli Llly and CompanyIndianapolisINUSA School of StatisticsEast China Normal UniversityShanghaiPeople's Republic of China Department of StatisticsUniversity of WisconsinMadisonWIUSA 

出 版 物：《Statistical Theory and Related Fields》 (统计理论及其应用（英文）)

年 卷 期：2023年第7卷第2期

页      面：159-163页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：This work was supported by National Institute of Allergy and Infectious Diseases[NIAID 5 UM1 AI068617]. 

主　　题：G-computation modelassisted nonlinear covariate adjustment risk difference logistic regression standardization 

摘      要：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.How-ever,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.