Yale University

Fitting additive hazards models for case-cohort studies: a multiple imputation approach.

TitleFitting additive hazards models for case-cohort studies: a multiple imputation approach.
Publication TypeJournal Article
Year of Publication2015
AuthorsJung, Jinhyouk, Ofer Harel, and Sangwook Kang
JournalStatistics in medicine
Date Published2015 Jul 20
ISSN1097-0258
AbstractIn 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.
DOI10.1002/sim.6588
Alternate JournalStat Med

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