Primary Submission Category: Randomized Designs and Analyses
Distributionally Equivalent Urns for the Truncation by Death Problem
Authors: Jaffer Zaidi, Tyler VanderWeele,
Presenting Author: Jaffer Zaidi*
The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics and causal inference. The survivor average causal effect is commonly identified with more assumptions than those guaranteed by the design of a randomized clinical trial. This paper demonstrates that individual level causal effects in the `always survivor’ principal stratum can be identified and quantified with no stronger identification assumptions than randomization. Distributionally equivalent sufficient cause urns are defined and developed on sufficient condition regions to quantify individual level ‘always survivor’ causal effects under truncation by death. Such urn models also enable sensitivity and multiverse analysis at the individual and population level, as well as enable comparison of different identification strategies. We illustrate the practical utility of our methods using data from randomized clinical trials in oncology and laser surgery in perinatal studies. Our comprehensive methodology is the first and, as of yet, only proposed procedure that enables quantifying individual level causal effects in the presence of truncation by death and censoring using only the assumptions that are guaranteed by design of the clinical trial.