Artikel
The challenge of time-to-event analysis for multiple events
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Autoren
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Ann-Kathrin Ozga
- Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
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Oliver Kuß
- German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Institute for Biometrics and Epidemiology, Düsseldorf, Germany
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Alexandra Nießl
- Institute of Statistics, Ulm University, Ulm, Germany
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Sandra Frank
- Institute of Statistics, Ulm University, Ulm, Germany
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Annika Hoyer
- Department of Statistics, Ludwig Maximilians University, Munich, Germany
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Gerrit Toenges
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| Veröffentlicht: | 24. September 2021 |
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Gliederung
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Introduction: The idea of this dataset challenge is that a variety of researchers introduce different approaches for the analysis of time-to-event data for multiple, possible recurrent, events applied on the same example-dataset. Ideally, this results in interesting discussions and shows the variety of alternative analysis methods for a more adequate description of the data at hand.
We aim to shed light on the research area of time-to-event analysis and to set the basis to establish maybe more adequate types of analysis for capturing disease processes holistically.
Methods: All analyses that will be shown are based on follow-up data of the Interdisciplinary Network for Heart Failure (INH) study [1]. This multi-centre randomized controlled trial investigates the efficacy of a nurse-coordinated disease management program (HNC) in heart failure compared to usual care for patients that were first hospitalized for systolic heart failure. In the original study [1] a composite of time to death or time to first rehospitalization was considered as primary endpoint. However, for this data set challenge, it was up to the groups involved here to use all available information on re-hospitalizations and on the competing event of death. A total of 1022 patients (513 in usual care, 509 in HNC group) with 663 deaths and 3016 rehospitalizations will be included in the analyzed dataset. The introduced analyses methods cover inter alia a joint frailty model for recurrent events with an associated terminal event for which SAS code by Toenges and Jahn-Eimermacher is used [2]. In addition, we will present a parametric, hazard-free model that generates the semi-competing risk model as described by Xu et al. [3] to the situation with recurrent events. A nonparametric approach with the extension from time-to-first endpoint to multistate models is the third method of choice.
Results: The introduced methods cover different aspects of the given data and therefore might lead to different results.
Discussion: However, we believe that they all are still more adequate in describing disease processes than the common time-to-first event analysis.
Conclusions: There is a huge variety of approaches that should be considered in future research for a more adequate analysis of complex time-to-event data, especially with recurrent events and a terminal endpoint.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
References
- 1.
- Angermann CE, Störk S, Gelbrich G, Faller H, Jahns R, Frantz S,et al. Mode of action and effects of standardized collaborative disease management on mortality and morbidity in patients with systolic heart failure: the Interdisciplinary Network for Heart Failure (INH) study. Circulation Heart Failure. 2012;5:25-35.
- 2.
- Toenges G, Jahn-Eimermacher A. Computational issues in fitting joint frailty models for recurrent events with an associated terminal event. Computer Methods and Programs in Biomedicine. 2019;188:105259.
- 3.
- Xu J, Kalbfleisch JD, Tai B. Statistical analysis of illness-death processes and semicompeting risks data. Biometrics. 2010;66(3):716-725.
