ArticleBMC medical research methodology2018
Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Attributable Length of Stay, Mortality Risk, and Costs of Bacterial Health Care-Associated Infections in Australia: A Retrospective Case-cohort Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
- Women With Diabetes Are at Increased Relative Risk of Heart Failure Compared to Men: Insights From UK Biobank.Frontiers in cardiovascular medicine · 2021Article
- A phase 2, open label, multicenter, single arm study of tocilizumab on the efficacy and tolerability of tocilizumab in the treatment of patients with COVID-19 pneumonia (TOCIVID-19 trial): Statistical analysis plan.Contemporary clinical trials communications · 2020Article
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- A review of the use of time-varying covariates in the Fine-Gray subdistribution hazard competing risk regression model.Statistics in medicine · 2020Review
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWe evaluate three methods for competing risks analysis with time-dependent covariates in comparison with the corresponding methods with time-independent covariates.
methodsWe 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.
resultsAll 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.
conclusionsThe 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.
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