TY - JOUR
T1 - Fitting additive hazards models for case-cohort studies
T2 - a multiple imputation approach
AU - Jung, Jinhyouk
AU - Harel, Ofer
AU - Kang, Sangwook
N1 - Publisher Copyright:
Copyright © 2015 John Wiley & Sons, Ltd.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2016/7/30
Y1 - 2016/7/30
N2 - 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.
AB - 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.
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U2 - 10.1002/sim.6588
DO - 10.1002/sim.6588
M3 - Article
C2 - 26194861
AN - SCOPUS:84977517675
VL - 35
SP - 2975
EP - 2990
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 17
ER -