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Multi-state Models for the Analysis of Survival Studies in Biomedical Research: An Alternative to Composite Endpoints

Primary endpoints of survival studies in biomedical research are usually composite endpoints, which indicate whether any of a list of events is observed. They are practical to empower studies and in the presence of competing risks, although constrained. In this work, we propose a more sophisticated modelization of the evolution of the disease for a patient with multi-state models, which allow to define relationships between adverse events by a state structure. Each transition between states may depend on different covariates, which provides a personalized prediction for patients, considering their characteristics, treatment and observed disease evolution. In order to illustrate their performance, we analyze a study in interventional cardiology including 1008 patients with acute coronary syndrome who underwent percutaneous revascularization between 2013 and 2019. The results show the great potential of multi-states models for analyzing survival studies in biomedical