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Causal inference with time-to-event outcomes under competing risk

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  • Jon Michael Gran

    (Oslo Centre for Biostatistics and Epidemiology (OCBE))

Abstract

The occurrence of competing events often complicate the analysis of time-to-event outcomes. While there is a rich literature in the area of survival analysis on methods for handling competing risk that goes back a long way, there has also for a long time been some confusion regarding best approach and implementation when facing competing events in applied research. Recent advances in the use of estimands in causal inference has led to new developments and insights (and discussions) on how to best analyze time-to-event outcomes under competing risk. The role of classical statistical estimands are now better understood, and new causal estimands have been suggested for addressing more advanced causal questions. In this talk, I will briefly review this development and the estimation of the most basic estimands and discuss some extensions, such as when interest is in the effect of time-varying treatments.

Suggested Citation

  • Jon Michael Gran, "undated". "Causal inference with time-to-event outcomes under competing risk," Northern European Stata Conference 2024 08, Stata Users Group.
    • Handle: RePEc:boc:neur24:08
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    File URL: http://repec.org/neur2024/Northern_Europe24_Gran.pdf
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    References listed on IDEAS

    as
    1. Jan Beyersmann & Christine Schrade, 2017. "Florence Nightingale, William Farr and competing risks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 285-293, January.
    2. Mats J. Stensrud & Jessica G. Young & Vanessa Didelez & James M. Robins & Miguel A. Hernán, 2022. "Separable Effects for Causal Inference in the Presence of Competing Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 175-183, January.
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