Vanessa Didelez: Causal reasoning in survival and time-to-event analyses

"Causal reasoning in survival and time-to-event analyses"
Vanessa Didelez, University of Bremen
Discussant: Els Goetghebeur (Ghent University)
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.
December 1, 2020