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Activity Number: 280 - New Methodology Developments in Analyzing Complex Survival Data
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Lifetime Data Science Section
Abstract #322303
Title: A Flexible, Computationally Efficient, Unified Proportional Hazards Model for Especially Structured Survival Data
Author(s): Prabhashi Withana Gamage* and Christopher McMahan and Lianming Wang
Companies: James Madison University and Clemson University and University of South Carolina
Keywords: Censored data; EM algorithm; Interval-censored data; Monotone splines; Proportional hazards model
Abstract:

Censored data naturally arise from many clinical and epidemiological studies when the event of interest is not observed but rather is known relevant to an observation time(s). In addition, some studies institute enrollment criterion that exclude participants who have experienced the event prior to being enrolled in the study. To analyze aforementioned data, we propose a novel, flexible, unified proportional hazards model that can be used to accommodate censored outcomes and/or studies with “enrollment issue.” An easy to implement expectation-maximization (EM) algorithm was developed for model fitting through a novel data augmenting process. The proposed model adopts a monotone spline representation for the purposes of approximating the unknown conditional cumulative baseline hazard function, significantly reducing the number of unknown parameters while retaining modeling flexibility. The performance of our methodology is evaluated through simulation studies and is further illustrated through real-world data sets.


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

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