Methods for analyzing recurrent and terminal events considering composite and competing endpoints in clinical trials: a practical overview

Time to event endpoints like time to myocardial infarction, relapse, or death are often of primary interest in clinical trials. To describe the whole disease process and gain a complete picture of treatment effects, methods for recurrent time to event analysis considering terminal events were proposed, i.e. methods considering all events that occur per individual. However, these methods are not often applied in clinical studies. This might be due to the researchers' uncertainties about underlying model assumptions and interpretation of results. We therefore aim to give a comprehensive overview of methods for the analysis of multiple time to events comparing model assumptions and interpretation. We illustrate the methods using a cardiovascular trial and provide the corresponding R code.

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Publication Details

Journal
BMC Medical Research Methodology
Published
2026-09-12
DOI
https://doi.org/10.1186/s12874-026-03008-7
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Methods for analyzing recurrent and terminal events considering composite and competing endpoints in clinical trials: a practical overview

Ann‐Kathrin Ozga, Alidan Duoerkongjiang, Paulus Kirchhof, Antonia Zapf
BMC Medical Research Methodology
Advanced Causal Inference Techniques
article

Methods for analyzing recurrent and terminal events considering composite and competing endpoints in clinical trials: a practical overview

Ann‐Kathrin Ozga, Alidan Duoerkongjiang, Paulus Kirchhof, Antonia Zapf
article en

Abstract

Time to event endpoints like time to myocardial infarction, relapse, or death are often of primary interest in clinical trials. To describe the whole disease process and gain a complete picture of treatment effects, methods for recurrent time to event analysis considering terminal events were proposed, i.e. methods considering all events that occur per individual. However, these methods are not often applied in clinical studies. This might be due to the researchers' uncertainties about underlying model assumptions and interpretation of results. We therefore aim to give a comprehensive overview of methods for the analysis of multiple time to events comparing model assumptions and interpretation. We illustrate the methods using a cardiovascular trial and provide the corresponding R code.

BMC Medical Research MethodologyVol. 26(1)
Universität Hamburg (DE), University Medical Center Hamburg-Eppendorf (DE)
Deutsches Zentrum für Herz-Kreislaufforschung, European Commission, Deutsche Forschungsgemeinschaft, Horizon 2020 Framework Programme
Good health and well-being
Openalex Percentile: Top 8%
Advanced Causal Inference Techniques
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