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 

机构地区：[1]Department of Biostatistics,University of Washington,Seattle,WA,USA [2]Global Statistical Sciences,Eli Llly and Company,Indianapolis,IN,USA [3]School of Statistics,East China Normal University,Shanghai,People's Republic of China [4]Department of Statistics,University of Wisconsin,Madison,WI,USA

出　　处：《Statistical Theory and Related Fields》2023年第2期159-163,共5页统计理论及其应用（英文）

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

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.

关 键 词：G-computation modelassisted nonlinear covariate adjustment risk difference logistic regression STANDARDIZATION 

分 类 号：O17[理学—数学]