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Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes
BACKGROUND: We evaluate three methods for competing risks analysis with time-dependent covariates in comparison with the corresponding methods with time-independent covariates. METHODS: We used cause-specific hazard analysis and two summary approaches for in-hospital death: logistic regression and r...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048847/ https://www.ncbi.nlm.nih.gov/pubmed/30012114 http://dx.doi.org/10.1186/s12874-018-0535-5 |
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author | Poguntke, Inga Schumacher, Martin Beyersmann, Jan on behalf of COMBACTE-MAGNET Consortium, Martin Wolkewitz |
author_facet | Poguntke, Inga Schumacher, Martin Beyersmann, Jan on behalf of COMBACTE-MAGNET Consortium, Martin Wolkewitz |
author_sort | Poguntke, Inga |
collection | PubMed |
description | BACKGROUND: We evaluate three methods for competing risks analysis with time-dependent covariates in comparison with the corresponding methods with time-independent covariates. METHODS: We used cause-specific hazard analysis and two summary approaches for in-hospital death: logistic regression and regression of the subdistribution hazard. We analysed real hospital data (n=1864) and considered pneumonia on admission / hospital-acquired pneumonia as time-independent / time-dependent covariates for the competing events ’discharge alive’ and ’in-hospital death’. Several simulation studies with time-constant hazards were conducted. RESULTS: All approaches capture the effect of time-independent covariates, whereas the approaches perform differently with time-dependent covariates. The subdistribution approach for time-dependent covariates detected effects in a simulated no-effects setting and provided counter-intuitive effects in other settings. CONCLUSIONS: The extension of the Fine and Gray model to time-dependent covariates is in general not a helpful synthesis of the cause-specific hazards. Cause-specific hazard analysis and, for uncensored data, the odds ratio are capable of handling competing risks data with time-dependent covariates but the use of the subdistribution approach should be neglected until the problems can be resolved. For general right-censored data, cause-specific hazard analysis is the method of choice. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0535-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6048847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60488472018-07-19 Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes Poguntke, Inga Schumacher, Martin Beyersmann, Jan on behalf of COMBACTE-MAGNET Consortium, Martin Wolkewitz BMC Med Res Methodol Research Article BACKGROUND: We evaluate three methods for competing risks analysis with time-dependent covariates in comparison with the corresponding methods with time-independent covariates. METHODS: We used cause-specific hazard analysis and two summary approaches for in-hospital death: logistic regression and regression of the subdistribution hazard. We analysed real hospital data (n=1864) and considered pneumonia on admission / hospital-acquired pneumonia as time-independent / time-dependent covariates for the competing events ’discharge alive’ and ’in-hospital death’. Several simulation studies with time-constant hazards were conducted. RESULTS: All approaches capture the effect of time-independent covariates, whereas the approaches perform differently with time-dependent covariates. The subdistribution approach for time-dependent covariates detected effects in a simulated no-effects setting and provided counter-intuitive effects in other settings. CONCLUSIONS: The extension of the Fine and Gray model to time-dependent covariates is in general not a helpful synthesis of the cause-specific hazards. Cause-specific hazard analysis and, for uncensored data, the odds ratio are capable of handling competing risks data with time-dependent covariates but the use of the subdistribution approach should be neglected until the problems can be resolved. For general right-censored data, cause-specific hazard analysis is the method of choice. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0535-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-16 /pmc/articles/PMC6048847/ /pubmed/30012114 http://dx.doi.org/10.1186/s12874-018-0535-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Poguntke, Inga Schumacher, Martin Beyersmann, Jan on behalf of COMBACTE-MAGNET Consortium, Martin Wolkewitz Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title | Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title_full | Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title_fullStr | Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title_full_unstemmed | Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title_short | Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
title_sort | simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048847/ https://www.ncbi.nlm.nih.gov/pubmed/30012114 http://dx.doi.org/10.1186/s12874-018-0535-5 |
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