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Understanding competing risks: a simulation point of view

Please always quote using this URN: urn:nbn:de:bvb:20-opus-142811
  • Background: Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. Methods: We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type.Background: Competing risks methodology allows for an event-specific analysis of the single components of composite time-to-event endpoints. A key feature of competing risks is that there are as many hazards as there are competing risks. This is not always well accounted for in the applied literature. Methods: We advocate a simulation point of view for understanding competing risks. The hazards are envisaged as momentary event forces. They jointly determine the event time. Their relative magnitude determines the event type. 'Empirical simulations' using data from a recent study on cardiovascular events in diabetes patients illustrate subsequent interpretation. The method avoids concerns on identifiability and plausibility known from the latent failure time approach. Results: The 'empirical simulations' served as a proof of concept. Additionally manipulating baseline hazards and treatment effects illustrated both scenarios that require greater care for interpretation and how the simulation point of view aids the interpretation. The simulation algorithm applied to real data also provides for a general tool for study planning. Conclusions: There are as many hazards as there are competing risks. All of them should be analysed. This includes estimation of baseline hazards. Study planning must equally account for these aspects.show moreshow less

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Metadaten
Author: Arthur Allignol, Martin Schumacher, Christoph Wanner, Christiane Drechsler, Jan Beyersmann
URN:urn:nbn:de:bvb:20-opus-142811
Document Type:Journal article
Faculties:Medizinische Fakultät / Medizinische Klinik und Poliklinik I
Language:English
Parent Title (English):BMC Medical Research Methodology
Year of Completion:2011
Volume:11
Issue:86
Pagenumber:1-13
Source:BMC Medical Research Methodology 2011 11:86.
DOI:https://doi.org/10.1186/1471-2288-11-86
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:Clinical-trials; Cumulative incidence function; Hazards; Model; Probabilities; Regression; Sample-sizes; Subdistribution; Tests
Release Date:2019/02/06
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung