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Online Causal Inference Seminar

Causal Inference Diagram
December 1, 2020 - 8:30am
online

Free and open to the public

Webinar link

All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin).

Tuesday, December 1, 2020 [Link to join] (webinar password: 007080)

  • Speaker: Vanessa Didelez (University of Bremen)
  • Discussant: Els Goetghebeur (Ghent University)
  • Title: Causal reasoning in survival and time-to-event analyses
  • Abstract In this talk I will discuss why causal inference should pay special attention to survival and time-to-event settings. Even in an apparently simple case of a randomized point-treatment it is common that events other than the event of interest occur, sometimes called intercurrent events such as (semi-)competing events or time-varying mediators, and of course censoring. The choice of causal estimand in such situations should anticipate these issues and suitably represent the research question. Recently, we have proposed so-called ''separable effects'', which focus on contrasts relating to different components of treatment (or exposure) that can be manipulated separately. This approach provides practically relevant estimands for various applications faced with time-varying mediation or competing events.
    Mostly, however, in time-to-event settings, treatments or exposures themselves are time-dependent (start / stop / switch treatment etc.); or we may, more generally, be interested in the causal relations among various types of events or processes. This entails potential sources of time-related biases such as time-dependent confounding or self-inflicted biases such as immortal-time bias. I will discuss a class of graphical models representing dynamic relations between processes which can help with causal reasoning in time-to-event settings and shed light onto some of the issues.
    Examples from the field of cancer research will be given.
    The presentation will focus on basic principles and concepts rather than technical details.
    Papers
    1) Didelez (2019). Defining causal mediation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis 25, 593-610. https://link.springer.com/article/10.1007/s10985-018-9449-0
    2) Didelez (2015). Causal Reasoning for events in continuous time: a decision-theoretic approach. Proceedings of the 31st Annual Conference on Uncertainty in Artificial Intelligence - Causality Workshop. http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/didelez.pdf
    3) Aalen, Stensrud, Didelez, Daniel, Røysland, Strohmaier (2020). Time-dependent mediators in survival analysis: Modelling direct and indirect effects with the additive hazards model. Biometrical Journal, 62, 532-549. https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201800263
    4) Stensrud, Young, Didelez, Robins & Hernán (2020) Separable effects for causal inference in the presence of competing events, JASA (online). https://www.tandfonline.com/doi/abs/10.1080/01621459.2020.1765783

This event belongs to the following series