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

Jinhyouk Jung, Ofer Harel, Sangwook Kang

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)2975-2990
Number of pages16
JournalStatistics in Medicine
Volume35
Issue number17
DOIs
Publication statusPublished - 2016 Jul 30

Fingerprint

Additive Hazards Model
Cohort Study
Multiple Imputation
Imputation
Proportional Hazards Models
Cohort Studies
Rejection Sampling
Modeling
Rejection Method
Missing Covariates
Missing at Random
Misspecification
Incomplete Data
Sampling Methods
Covariates
Cancer
Regression
Simulation Study
Neoplasms

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Cite this

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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 journalArticle

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