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Statistics Seminar - Semiparametric Additive Hazards Regression Models for Case-Cohort/Two-Phase Sampling Designs

Event Type
Annie Qu
165 Everitt
Apr 3, 2014   4:00 - 5:15 pm  
Dr. Yanqing Sun (University of North Carolina at Charlotte)

Under the case-cohort design introduced by Prentice (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, which 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, which is more efficient than the widely adopted inverse

probability weighted complete-case estimation method. The asymptotic properties of the proposed estimators are established, and the finite-sample properties are examined through an extensive simulation study. The method is applied to analyze data from a preventive HIV vaccine efficacy trial.

This is a joint work with Xiyuan Qian, Qiong Shou, and Peter Gilbert.

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