Proportional hazards model with a change point for clustered event data.

TitleProportional hazards model with a change point for clustered event data.
Publication TypeJournal Article
Year of Publication2017
AuthorsDeng, Yu, Donglin Zeng, Jinying Zhao, and Jianwen Cai
JournalBiometrics
Volume73
Issue3
Pagination835-845
Date Published2017 09
ISSN1541-0420
KeywordsCluster Analysis, Likelihood Functions, Proportional Hazards Models, Risk Factors
Abstract

In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating the regression coefficients and the unknown change point. We develop a supremum test based on robust score statistics to test the existence of the change point. The inference for the change point is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The finite-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods.

DOI10.1111/biom.12655
Alternate JournalBiometrics
Original PublicationProportional hazards model with a change point for clustered event data.
PubMed ID28257142
PubMed Central IDPMC5582026
Grant ListK01 AG034259 / AG / NIA NIH HHS / United States
R01 DK091369 / DK / NIDDK NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
R01 HL109284 / HL / NHLBI NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
R01 DK107532 / DK / NIDDK NIH HHS / United States