Title | Parameter estimation in Cox models with missing failure indicators and the OPPERA study. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Brownstein, Naomi C., Jianwen Cai, Gary D. Slade, and Eric Bair |
Journal | Stat Med |
Volume | 34 |
Issue | 30 |
Pagination | 3984-96 |
Date Published | 2015 Dec 30 |
ISSN | 1097-0258 |
Keywords | Biostatistics, 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. |
DOI | 10.1002/sim.6604 |
Alternate Journal | Stat Med |
Original Publication | Parameter estimation in Cox models with missing failure indicators and the OPPERA study. |
PubMed ID | 26242613 |
PubMed Central ID | PMC4715503 |
Grant List | R01ES021900 / 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 |
Parameter estimation in Cox models with missing failure indicators and the OPPERA study.
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