Improving the efficiency of estimation in the additive hazards model for stratified case-cohort design with multiple diseases.

TitleImproving the efficiency of estimation in the additive hazards model for stratified case-cohort design with multiple diseases.
Publication TypeJournal Article
Year of Publication2016
AuthorsKim, Soyoung, Jianwen Cai, and David Couper
JournalStat Med
Volume35
Issue2
Pagination282-93
Date Published2016 Jan 30
ISSN1097-0258
KeywordsBiostatistics, Cohort Studies, Computer Simulation, Coronary Disease, Cyclooxygenase 1, Disease, Humans, Multivariate Analysis, Proportional Hazards Models, Risk Factors, Stroke
Abstract

The case-cohort study design has often been used in studies of a rare disease or for a common disease with some biospecimens needing to be preserved for future studies. A case-cohort study design consists of a random sample, called the subcohort, and all or a portion of the subjects with the disease of interest. One advantage of the case-cohort design is that the same subcohort can be used for studying multiple diseases. Stratified random sampling is often used for the subcohort. Additive hazards models are often preferred in studies where the risk difference, instead of relative risk, is of main interest. Existing methods do not use the available covariate information fully. We propose a more efficient estimator by making full use of available covariate information for the additive hazards model with data from a stratified case-cohort design with rare (the traditional situation) and non-rare (the generalized situation) diseases. We propose an estimating equation approach with a new weight function. The proposed estimators are shown to be consistent and asymptotically normally distributed. Simulation studies show that the proposed method using all available information leads to efficiency gain and stratification of the subcohort improves efficiency when the strata are highly correlated with the covariates. Our proposed method is applied to data from the Atherosclerosis Risk in Communities study.

DOI10.1002/sim.6623
Alternate JournalStat Med
Original PublicationImproving the efficiency of estimation in the additive hazards model for stratified case-cohort design with multiple diseases.
PubMed ID26310388
PubMed Central IDPMC4715780
Grant ListR01ES021900 / ES / NIEHS NIH HHS / United States
HHSN268201100012C / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100005C / / PHS HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100009C / / PHS HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
HHSN268201100010C / / PHS HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
HHSN268201100008C / / PHS HHS / United States
HHSN268201100012C / / PHS HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100007C / / PHS HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100011C / / PHS HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
HHSN268201100006C / / PHS HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
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