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.
Original language | English |
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Pages (from-to) | 2975-2990 |
Number of pages | 16 |
Journal | Statistics in Medicine |
Volume | 35 |
Issue number | 17 |
DOIs | |
Publication status | Published - 2016 Jul 30 |
Bibliographical note
Publisher Copyright:Copyright © 2015 John Wiley & Sons, Ltd.
All Science Journal Classification (ASJC) codes
- Epidemiology
- Statistics and Probability