Fitting additive hazards models for case‐cohort studies: a multiple imputation approach |
| |
Authors: | Jinhyouk Jung Ofer Harel Sangwook Kang |
| |
Affiliation: | 1. Deloitte Consulting, Seoul, Korea;2. Department of Statistics, University of Connecticut, Storrs, CT, U.S.A.;3. Department of Applied Statistics, Yonsei University, Seoul, Korea |
| |
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. |
| |
Keywords: | additive hazards model missing by design multiple imputation rejection sampling survival analysis |
|
|