title: Parameter estimation in Cox models with missing failure indicators and the OPPERA study.
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.
authors.affiliation: Ion Cyclotron Resonance Facility, National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, U.S.A.|Department of Statistics, Florida State University, Tallahassee, FL, U.S.A.
authors.forename: Naomi C
authors.initials: NC
authors.lastname: Brownstein
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Jianwen
authors.initials: J
authors.lastname: Cai
authors.affiliation: School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Gary D
authors.initials: GD
authors.lastname: Slade
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.|School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
authors.forename: Eric
authors.initials: E
authors.lastname: Bair
mesh_terms: D056808:Biostatistics; D015331:Cohort Studies; D003198:Computer Simulation; D005157:Facial Pain / Q000453:epidemiology / Q000209:etiology; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013705:Temporomandibular Joint Disorders / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology
keywords: Cox regression; Poisson regression; missing data; multiple imputation; survival analysis