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Activity Number: 24 - Causal Inference When the Outcome Is Truncated by Death
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Marketing
Abstract #312188
Title: Principal Surrogate Evaluation Using Multiple Trials
Author(s): Zhichao Jiang* and Peng Ding and Zhi Geng
Companies: University of Massachusetts, Amherst and University of California, Berkeley and Peking University
Keywords: Causal inference; Surrogate evaluation; Principal stratification

In randomized trials, surrogates which allow one to predict the effect of the treatment on the outcome of interest from the effect of the treatment on the surrogate are important when it is time-consuming or expensive to measure the primary outcome. Unfortunately, it is possible that the effect of the treatment on the surrogate is positive, the surrogate and outcome are strongly positively correlated, but the effect of the treatment on the outcome is negative. This phenomenon is referred to as the ``surrogate paradox’’ and may lead to disasters in practice. In this talk, I will introduce our new criteria for surrogates to avoid the surrogate paradox based on the principal stratification framework. The evaluation of these criteria requires the identification of principal causal effects. However, we cannot assume the commonly used exclusion restriction assumption for identification. We propose a new identification strategy by utilizing the homogeneity across the multiple trials from a motivating colon cancer clinical dataset. We also develop estimation and inference methods and a model checking procedure. Finally, we apply the method to evaluate whether three-year recurrence of ca

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

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