Incorporating Diagnostic Accuracy into the Estimation of Discrete Survival Function with Lost-to-Follow-Up

• Activity Number: 561
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Abidemi Adeniji*+ and Abdus Wahed
• Companies: Boehringer Ingelheim and University of Pittsburgh
• Keywords: Misclassi?cation ; Measurement error ; Diagnostic testing ; Product limit estimation ; Estimating equations ; Binary classi?cation

Abstract

The product limit or Kaplan-Meier (KM) estimator is commonly used to estimate the survival function when follow-up time is incomplete due to drop-outs. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values (NPV) and positive predictive values (PPV), into a Kaplan-Meier-like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided and these properties are examined through simulations. We demonstrate our methods using data from the VIRAHEP-C study.