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Activity Number: 531
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #319369
Title: Analyzing Semi-Competing Risks Data with Missing Cause of Informative Terminal Event
Author(s): Hong Zhu* and Renke Zhou and Melissa Bondy and Jing Ning
Companies: The University of Texas Southwestern Medical Center and Baylor College of Medicine and Baylor College of Medicine and MD Anderson Cancer Center
Keywords: Copula model ; EM algorithm ; Informative censoring ; Missing cause of failure ; Semi-competing risks

Cancer studies frequently yield multiple event times that correspond to landmarks in disease progression, including non-terminal events (i.e., cancer recurrence) and an informative terminal event (i.e., cancer-related death). Hence, we often observe semi-competing risks data. Work on such data has focused on scenarios in which the cause of the terminal event is known. However, in some circumstances, information on cause for patients who experience the terminal event is missing; consequently, we are not able to differentiate an informative terminal event from a non-informative terminal event. We propose a method to handle missing data regarding the cause of an informative terminal event when analyzing the semi-competing risks data. We first consider the nonparametric estimation of the survival function for the terminal event time given missing cause-of-failure data via the Expectation-Maximization (EM) algorithm. We then develop an estimation method for semi-competing risks data with missing cause of the terminal event, under a pre-specified semiparametric copula model. We conduct simulation studies and illustrate our methodology using early-stage breast cancer study data.

Authors who are presenting talks have a * after their name.

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