AI Article Synopsis
- Simulation studies are essential for testing statistical models used in analyzing complicated survival data, especially when dealing with competing risks and clustering.
- The article guides researchers on generating competing risks data and inducing cluster-level correlation, specifically for randomized clinical trials involving binary treatments.
- It reviews various methods, including the frailty model, probability transform, and Moran's algorithm, to create complex survival outcomes while maintaining specified levels of correlation.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654122 | PMC |
| http://dx.doi.org/10.1177/09622802221136067 | DOI Listing |
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