Nonparametric Survival Function Estimation in the Presence of Uncertain Endpoints by Using an Internal Validation Subsample

• Activity Number: 320
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jarcy Zee*+ and Sharon X. Xie
• Companies: University of Pennsylvania Perelman School of Medicine and University of Pennsylvania Perelman School of Medicine
• Keywords: nonparametric survival ; uncertain endpoint ; missing survival outcome ; failure time ; measurement error ; validation sample

Abstract

When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop a likelihood function for the situation where we have both uncertain endpoints for all patients and true endpoints for only a subset of patients. We numerically solve for a nonparametric maximum likelihood estimate of the survival function of time to the true endpoint. Using a simulation study, we compare our estimator to the complete data Kaplan-Meier survival function estimator to show the proposed estimator works well in moderate sample sizes. Finally, we illustrate the method with an example in Alzheimer's disease.