Recurrent Events Data with Missing Event Category

• Activity Number: 672
• Type: Invited
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Jianwen Cai* and Feng-Chang Lin and Jason Fine and Huichuan J. Lai
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and University of Wisconsin - Madison
• Keywords: recurrent events ; missing event category ; rate proportion ; local polynomial regression ; Nelson-Aalen estimation ; cystic fibrosis

Abstract

Recurrent event data frequently arise in longitudinal studies when study subjects possibly experience more than one event during the observation period. Often, such recurrent events can be categorized. However, part of the categorization may be missing due to technical difficulties or recording ignorance. If the event types are missing completely at random, then a complete case analysis may provide consistent estimates of regression parameters in certain regression models, but estimates of the baseline event rates are generally biased. Previous work on nonparametric estimation of these rates has utilized parametric missingness models. We consider fully nonparametric methods in which the missingness mechanism is completely unspecified. Consistency and asymptotic normality of the nonparametric estimators of the mean event functions accommodate nonparametric estimators of the event category probabilities which converge more slowly than the parametric rate. Plug-in variance estimators are provided and perform well in simulation studies. The proposed methods are applied to the cystic fibrosis registry data.