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Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort
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Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort
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Journal Article
Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort
2022
Overview
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.
Publisher
MDPI AG,MDPI
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