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Activity Number: 104 - Survival Analysis in Causal Inference Studies
Type: Invited
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Lifetime Data Science Section
Abstract #308098
Title: Detecting Individual-Level 'Always Survivor' Causal Effects Under 'Truncation by Death' and Censoring Through Time
Author(s): Jaffer Zaidi* and Eric Tchetgen Tchetgen and Tyler VanderWeele
Companies: University of North Carolina, Chapel Hill and University of Pennsylvania and Harvard T.H. Chan School of Public Health
Keywords: Truncation by death; competing risks; direct effects; randomization; survival analysis; causal inference

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 or using sensitivity analysis. This paper demonstrates that individual level causal effects in the `always survivor' principal stratum can be identified with no stronger identification assumptions than randomization. We illustrate the practical utility of our methods using data from a clinical trial on patients with prostate cancer. Our methodology is the first and, as of yet, only proposed procedure that enables detecting causal effects in the presence of truncation by death using only the assumptions that are guaranteed by design of the randomized clinical trial.

Authors who are presenting talks have a * after their name.

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