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 |
|---|---|
| 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 |
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All Science Journal Classification (ASJC) codes
- Epidemiology
- Statistics and Probability
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Fitting additive hazards models for case-cohort studies : a multiple imputation approach. / Jung, Jinhyouk; Harel, Ofer; Kang, Sangwook.
In: Statistics in Medicine, Vol. 35, No. 17, 30.07.2016, p. 2975-2990.Research output: Contribution to journal › Article
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
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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UR - http://www.scopus.com/inward/citedby.url?scp=84977517675&partnerID=8YFLogxK
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 -