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Activity Number: 208 - Personalized and Precision Medicine
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Biometrics Section
Abstract #318335
Title: Relative Contrast Estimation and Inference for Treatment Recommendation
Author(s): Muxuan Liang* and Menggang Yu
Companies: Fred Hutchinson Cancer Research Center and University of Wisconsin
Keywords: Observational study; Precision medicine; Semiparametric efficiency; Single index model
Abstract:

When there are resource constraints, it is important to rank or estimate treatment benefits according to patient characteristics. This facilitates prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as the metric for benefits. However, there can be settings where relative differences may better represent such benefits. In this paper, we consider modeling such relative differences that form scale-invariant contrasts between conditional treatment effects. We show that all scale-invariant contrasts are monotonic transformations of each other. Therefore we posit a single index model for a particular relative contrast. We then derive estimating equations and efficient scores via semiparametric efficiency theory. Based on the efficient score and its variant, we propose a two-step approach including a doubly robust loss function and a subsequently one-step de-bias procedure. Theoretical properties of the estimation and inference procedures are provided. Simulations and real data example are conducted to demonstrate the proposed approach.


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

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