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

Autor:	Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	g-computation model-assisted nonlinear covariate adjustment risk difference logistic regression standardization Probabilities. Mathematical statistics QA273-280
Zdroj:	Statistical Theory and Related Fields, Vol 7, Iss 2, Pp 159-163 (2023)
Druh dokumentu:	article
ISSN:	2475-4269 2475-4277 24754269
DOI:	10.1080/24754269.2023.2205802
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fc63c682afa146028d53a521eacfc10a Zobrazit plný text záznamu View record in DOAJ