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.Comment: Version 4: 23 pages, 0 figure
