Title: Beyond Always-Survivors: Population Causal Effects for Outcomes Truncated by Death
Abstract: In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent outcomes such as survival. Standard causal estimands, such as the survivor average causal effect, focus on a nonidentifiable subgroup and therefore can be difficult to interpret. These estimands are also complicated to identify in longitudinal settings with repeated measurements or time-varying confounders. We address these challenges by introducing a novel set of estimands that (i) concern the entire population, (ii) remain causally interpretable, and (iii) fully utilize the longitudinal data commonly available in studies with outcomes truncated by death. Furthermore, we extend these results to construct a new class of single-world estimands, generalizing existing results on separable effects. We illustrate the approach through a reanalysis of a prostate cancer trial, highlighting how different estimands can yield different treatment conclusions.
Biography: Linbo Wang is Canada Research Chair in Causal Machine Learning, and an associate professor in the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences, University of Toronto. He is also a faculty affiliate at the Vector Institute and holds affiliate positions in the Department of Statistics at the University of Washington and the Department of Computer Science at the University of Toronto. His research focuses on causality and its interaction with statistics and machine learning.
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