Quantifying Individual Level Always-Survivor Causal Effects Under Truncation by Death and Informative Censoring

Jaffer Zaidi



Date: Wednesday, September 2, 2020
Time: 4:00 pm - 5:00 pm

Abstract: 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. We demonstrate that individual level causal effects in the `always survivor’ principal stratum can be identified and quantified 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 methodology that enables detecting and quantifying causal effects in the presence of truncation by death and informative censoring using only the assumptions that are guaranteed by design of the randomized clinical trial.


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