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 clinical examination by an expert dentist. In a large study, examining all subjects in this manner is infeasible. Instead, it is common to use a cheaper (and less reliable) examination to screen for possible incident cases and perform the ``gold standard'' examination only on participants who screen positive on this simpler examination. Unfortunately, some subjects may leave the study before receiving the ``gold standard'' examination. Within the framework of survival analysis, this results in missing censoring 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 censoring indicators. We estimate the probability of being a case for those with no ``gold standard'' examination using logistic regression. These predicted probabilities are used to generate multiple imputations of each missing case status and estimate the hazard ratios associated with each putative risk factor. The variance introduced by the procedure is estimated using multiple imputation. We simulate data with missing censoring indicators and show that our method performs as well as or better than the competing methods. Finally, we apply the proposed method to analyze data from the OPPERA study.


Biostatistics | Survival Analysis