Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. (February 2023)

• Record Type: Journal Article
• Authors: Meng, Can; Esserman, Denise; Li, Fan; Zhao, Yize; Blaha, Ondrej; Lu, Wenhan; Wang, Yuxuan; Peduzzi, Peter; Greene, Erich J.
• Is Part Of: Statistical methods in medical research. Volume 32:Number 2(2023)
• Page Start: 305
• Page End: 333
• Publication Date: 2023-02
• Subjects: Simulation study -- time-to-event data -- cluster randomized trials -- competing risks -- semi-competing risks -- clustering; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221136067
• Languages: English
• ISSNs: 0962-2802

Abstract

Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran's algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.