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Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort
dc.contributor.author | Martínez-Camblor, Pablo | |
dc.date.accessioned | 2024-04-10T06:44:54Z | |
dc.date.available | 2024-04-10T06:44:54Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.3390/ijerph191912476 | |
dc.identifier.issn | 16617827 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/11077 | |
dc.description.abstract | Proportional hazard Cox regression models are overwhelmingly used for analyzing time-dependent outcomes. Despite their associated hazard ratio is a valuable index for the difference between populations, its strong dependency on the underlying assumptions makes it a source of misinterpretation. Recently, a number of works have dealt with the subtleties and limitations of this interpretation. Besides, a number of alternative indices and different Cox-type models have been proposed. In this work, we use synthetic data, motivated by a real-world problem, for showing the strengths and weaknesses of some of those methods in the analysis of time-dependent outcomes. We use the power of synthetic data for considering observable results but also utopian designs. © 2022 by the author. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | MDPI | es_ES |
dc.subject | Cox regression models | es_ES |
dc.subject | hazard ratios | es_ES |
dc.subject | marginal Cox regression models | es_ES |
dc.subject | survival analysis | es_ES |
dc.subject | time-to-event | es_ES |
dc.title | Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort | es_ES |
dc.type | Article | es_ES |