Jan Beyersmann - Can today's intention to treat have a causal effect on tomorrow's hazard function?

You are all invited to an exciting seminar at Biostats on Thursday November 30th @ 15:15.

Thursday, November 30 @ 15:15:

Speaker: Jan Beyersmann, Institute of Statistics, Ulm University

Title: "Can today's intention to treat have a causal effect on tomorrow's hazard function?”

Abstract: Hazards condition on previous survival, which makes them both identifiable based on censored data and the inferential key quantities of survival analysis. It also makes them subject to critique from a causal point of view. The worry is that after randomization of the intention to treat a more beneficial treatment will help sicker patients to survive longer, rendering treatment intention and markers of sickness dependent after time origin. Called ‘collider bias’, this is interpreted as breaking randomization and therefore complicating detection of a causal treatment effect. The strange part of this argument is that the situation at later times is explained as a causal consequence of treatment. I will try to review this dilemma - identifiability vs. causal concerns - and argue that there is a causal effect of today’s intention to treat on the future hazard function, if interpreted in a functional way. I will also argue that things are the way they should be and ‘collider bias’ really ‘collider effect’, that the latter has little to do with time-to-event, and that piecewise constant hazard ratios carry information on how treatment works.

My impression is that the debate is a bit pointed, but that there is general agreement that analyses of hazards - where the causal effect is hidden or perhaps obvious - should routinely be translated onto the probability scale. My worry is that these subtleties are lost in translation and I will illustrate matters with a (typical?) example from benefit-risk assessment in Germany, where a company managed to both claim a better and a worse safety profile of their drug, while only partially acknowledging the need to account for censoring. Time permitting, I will also discuss a multistate approach to g-computation motivated by a phase 3 trial of non-small-cell lung cancer patients where the experimental treatment was put on’(‘clinical’) hold by the FDA for some months shortly before recruitment was completed. The aim of the analysis is to estimate the survival distributions (sic) in the hypothetical scenario where the put-on-hold hazard is equated with zero (sic). The difficulty is that time-to-clinical-hold and time-to-death are not independent.

Room: Biostat library (5.2.46)

For more information and to see the past seminars go to https://biostatistics.dk/seminars/