Parameter estimation in Cox models with missing failure indicators and the OPPERA study.

TitleParameter estimation in Cox models with missing failure indicators and the OPPERA study.
Publication TypeJournal Article
Year of Publication2015
AuthorsBrownstein, Naomi C., Jianwen Cai, Gary D. Slade, and Eric Bair
JournalStat Med
Volume34
Issue30
Pagination3984-96
Date Published2015 Dec 30
ISSN1097-0258
KeywordsBiostatistics, Cohort Studies, Computer Simulation, Facial Pain, Female, Humans, Incidence, Logistic Models, Male, Poisson Distribution, Proportional Hazards Models, Prospective Studies, Risk Assessment, Risk Factors, Temporomandibular Joint Disorders
Abstract

In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study.

DOI10.1002/sim.6604
Alternate JournalStat Med
Original PublicationParameter estimation in Cox models with missing failure indicators and the OPPERA study.
PubMed ID26242613
PubMed Central IDPMC4715503
Grant ListR01ES021900 / ES / NIEHS NIH HHS / United States
UL1 TR001111 / TR / NCATS NIH HHS / United States
P30 ES010126 / ES / NIEHS NIH HHS / United States
T32ES007018 / ES / NIEHS NIH HHS / United States
P03ES010126 / ES / NIEHS NIH HHS / United States
UL1TR001111 / TR / NCATS NIH HHS / United States
T32 ES007018 / ES / NIEHS NIH HHS / United States
KL2 TR001109 / TR / NCATS NIH HHS / United States
R03DE023592 / DE / NIDCR NIH HHS / United States
R03 DE023592 / DE / NIDCR NIH HHS / United States
U01DE017018 / DE / NIDCR NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
U01 DE017018 / DE / NIDCR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
Project: