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

TL;DR

This paper introduces a new method for estimating parameters in Cox models when some failure indicators are missing, using multiple imputation based on logistic regression, demonstrated through simulations and application to the OPPERA study.

Contribution

The paper presents a novel multiple imputation approach for survival analysis with missing failure indicators, applicable to Cox models and incidence rate estimation.

Findings

Method performs as well or better than existing approaches in simulations.

Successfully applied to real data from the OPPERA study.

Provides a practical solution for handling missing failure data in cohort studies.

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.