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Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort

Author

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  • Pablo Martínez-Camblor

    (Biomedical Data Science Department, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
    Faculty of Health Sciences, Universidad Autonoma de Chile, Providencia 7500912, Chile)

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.

Suggested Citation

  • Pablo Martínez-Camblor, 2022. "Learning the Treatment Impact on Time-to-Event Outcomes: The Transcarotid Artery Revascularization Simulated Cohort," IJERPH, MDPI, vol. 19(19), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12476-:d:930064
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    References listed on IDEAS

    as
    1. Valentin Amrhein & David Trafimow & Sander Greenland, 2019. "Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 262-270, March.
    2. Thomas H. Scheike & Mei‐Jie Zhang, 2002. "An Additive–Multiplicative Cox–Aalen Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 75-88, March.
    3. Pablo Martínez‐Camblor & Todd A. MacKenzie & Douglas O. Staiger & Phillip P. Goodney & A. James O’Malley, 2019. "An instrumental variable procedure for estimating Cox models with non‐proportional hazards in the presence of unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(4), pages 985-1005, August.
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