[ML Seminar] IDA ML Seminar - Fredrik D. Johansson - Learning causally sound and interpretable composite endpoints for clinical trials

[Louis Ohl]

Welcome to an IDA Machine Learning Seminar on Wednesday, April 08 at 13:30 In Alan Turing

Learning causally sound and interpretable composite endpoints for clinical trials

Fredrik D. Johansson<https://www.fredjo.com/>, Chalmers University of Technology, Healthy AI Lab

Abstract: Randomized clinical trials are considered the gold standard evidence for learning about the causal effects of medical interventions, but have natural limitations on scope and length. This often rules out targeting long-term outcomes of interest, such as mortality or cardiovascular disease, as these endpoints won’t be observed for most participants during the length of the trial. Instead, researchers turn to surrogate endpoints that are associated with the primary outcome of interest and can be observed during the trial. This presents a problem: What constitutes a good surrogate? In theory, a good surrogate is one for which the effect of the treatment is predictive of its effect on the primary outcome, but the definition alone does not reveal how to find such a variable. More than that, to be useful in a clinical trial, the surrogate must be approved by a regulatory body when registering the trial, necessitating its interpretability. In this talk, I will discuss the implications of this, algorithms that can provably learn composite surrogates from observational data, and situations where there is no hope to find a good surrogate.

Location: Alan Turing, https://link.mazemap.com/RDyxFvXH

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Louis Ohl
Postdoc
louis.ohl at liu.se

Linköping University
IDA / STIMA
B-Huset, 2A:445
Webpage<https://oshillou.github.io/>
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