Analysis of two-phase sampling data with semiparametric additive hazards models

Yanqing Sun (), Xiyuan Qian (), Qiong Shou () and Peter B. Gilbert ()
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Yanqing Sun: University of North Carolina at Charlotte
Xiyuan Qian: East China University of Science and Technology
Qiong Shou: Merck China & Co., Inc.
Peter B. Gilbert: University of Washington and Fred Hutchinson Cancer Research Center

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 3, No 3, 377-399

Abstract: Abstract 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.

Keywords: 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 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10985-016-9363-2

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