Affiliation:
1. Department of Biostatistics, Yale University School of Public Health, New Haven, CT USA
2. Yale Center for Analytical Sciences, New Haven, CT USA
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.
Funder
Yale Clinical and Translational Science Award
Claude D. Pepper Older Americans Independence Center, Yale University
NIH
Subject
Health Information Management,Statistics and Probability,Epidemiology
Cited by
1 articles.
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