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Activity Number:
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322
- Novel Statistical Methods and Applications in Precision Mental Health
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #323403
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Title:
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Bayesian Index Models for Heterogeneous Treatment Effects
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Author(s):
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Hyung G. Park* and Danni Wu and Eva Petkova and Thaddeus Tarpey and R. Todd Ogden
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Companies:
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NYU School of Medicine and NYU School of Medicine and NYU School of Medicine and New York University and Columbia University
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Keywords:
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Bayesian single index models;
Heterogeneous treatment effects;
Precision medicine
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Abstract:
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We develop a Bayesian model with a flexible link function connecting an exponential family treatment response to a linear combination of covariates and a treatment indicator. Generalized linear models allowing data-driven link functions are often called "single-index models,” and among popular semi-parametric modeling methods. In this talk, based on a Bayesian version of generalized single-index models we will focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision mental health applications.
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Authors who are presenting talks have a * after their name.
