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Activity Number:
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116
- Epidemiological Models for Longitudinal Studies, Time-to-Event Outcomes, and Functional Data
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #322866
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Title:
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Quantifying Always Survivor Causal Effects Under Truncation by Death and Informative Censoring
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Author(s):
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Jaffer Zaidi*
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Companies:
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George Mason University
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Keywords:
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Truncation by death;
principal stratification;
randomization;
sufficient cause
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Abstract:
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The analysis of causal effects when the outcome of interest is possibly truncated by death has a long history in statistics. 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. We further develop Rothman's sufficient cause model to derive further results unifying different identification strategies for principal causal effects
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Authors who are presenting talks have a * after their name.
