Nonparametric Discrete Survival Function Estimation with Uncertain Endpoints Using an Internal Validation Subsample

• Activity Number: 386
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• 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: measurement error ; missing data ; nonparametric survival analysis ; uncertain endpoints ; 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 an estimated likelihood function for the situation where we have both uncertain endpoints for all participants and true endpoints for only a subset of participants. We propose a nonparametric maximum estimated likelihood estimator of the discrete survival function of time to the true endpoint. We show that the proposed estimator is consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias compared to the nave Kaplan-Meier survival function estimator, which uses only uncertain endpoints, and more ecient with moderate missingness compared to the complete-case Kaplan-Meier survival function estimator, which uses only available true endpoints. Finally, we apply the proposed method to a dataset for estimating the risk of developing Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative.