title: Fitting additive hazards models for case-cohort studies: a multiple imputation approach.
abstract: In this paper, we consider fitting semiparametric additive hazards models for case-cohort studies using a multiple imputation approach. In a case-cohort study, main exposure variables are measured only on some selected subjects, but other covariates are often available for the whole cohort. We consider this as a special case of a missing covariate by design. We propose to employ a popular incomplete data method, multiple imputation, for estimation of the regression parameters in additive hazards models. For imputation models, an imputation modeling procedure based on a rejection sampling is developed. A simple imputation modeling that can naturally be applied to a general missing-at-random situation is also considered and compared with the rejection sampling method via extensive simulation studies. In addition, a misspecification aspect in imputation modeling is investigated. The proposed procedures are illustrated using a cancer data example. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Deloitte Consulting, Seoul, 150-945, Korea.
authors.forename: Jinhyouk
authors.initials: J
authors.lastname: Jung
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, 06269, CT, U.S.A.
authors.forename: Ofer
authors.identifier: 0000-0002-1054-3055
authors.initials: O
authors.lastname: Harel
authors.affiliation: Department of Applied Statistics, Yonsei University, Seoul, 120-749, Korea.
authors.forename: Sangwook
authors.initials: S
authors.lastname: Kang
mesh_terms: D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D016016:Proportional Hazards Models
keywords: additive hazards model; missing by design; multiple imputation; rejection sampling; survival analysis