文献类型：期刊文献

英文题名：Analysis of two-phase sampling data with semiparametric additive hazards models

作者：Sun, Yanqing[1];Qian, Xiyuan[2];Shou, Qiong[3];Gilbert, Peter B.[4,5]

机构：[1]Univ North Carolina Charlotte, Dept Math & Stat, Charlotte, NC 28223 USA;[2]East China Univ Sci & Technol, Dept Math, Shanghai, Peoples R China;[3]Merck China & Co Inc, Beijing, Peoples R China;[4]Univ Washington, Seattle, WA 98109 USA;[5]Fred Hutchinson Canc Res Ctr, Seattle, WA 98109 USA

年份：2017

卷号：23

期号：3

起止页码：377

外文期刊名：LIFETIME DATA ANALYSIS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000403359100003)】；

基金：The authors thank Richard Wyatt, David Montefiori and John Mascola for measuring the immune response data for the VaxGen 004 trial. The authors thank the reviewers for their constructive comments that have improved the paper. Sun's research was partially supported by NSF Grants DMS-1208978 and DMS-1513072, NIH Grant R37 AI054165, and the Reassignment of Duties fund provided by the University of North Carolina at Charlotte. Gilbert's research was partially supported by NIH Grant R37 AI054165.

语种：英文

外文关键词：Asymptotics; Augmented inverse probability weighted estimation; Auxiliary variables; Double robustness; Efficiency; Estimating equations; HIV vaccine efficacy trial; Inverse probability weighted complete-case; Parametric regression; Time-varying effects

摘要：Under the case-cohort design introduced by Prentice (Biometrica 73:1-11, 1986), the covariate histories are ascertained only for the subjects who experience the event of interest (i.e., the cases) during the follow-up period and for a relatively small random sample from the original cohort (i.e., the subcohort). The case-cohort design has been widely used in clinical and epidemiological studies to assess the effects of covariates on failure times. Most statistical methods developed for the case-cohort design use the proportional hazards model, and few methods allow for time-varying regression coefficients. In addition, most methods disregard data from subjects outside of the subcohort, which can result in inefficient inference. Addressing these issues, this paper proposes an estimation procedure for the semiparametric additive hazards model with case-cohort/two-phase sampling data, allowing the covariates of interest to be missing for cases as well as for non-cases. A more flexible form of the additive model is considered that allows the effects of some covariates to be time varying while specifying the effects of others to be constant. An augmented inverse probability weighted estimation procedure is proposed. The proposed method allows utilizing the auxiliary information that correlates with the phase-two covariates to improve efficiency. The asymptotic properties of the proposed estimators are established. An extensive simulation study shows that the augmented inverse probability weighted estimation is more efficient than the widely adopted inverse probability weighted complete-case estimation method. The method is applied to analyze data from a preventive HIV vaccine efficacy trial.