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Activity Number:
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193
- Section on Medical Devices and Diagnostics: Student Paper Competition
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #322671
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Title:
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A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules
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Author(s):
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Lanqiu Yao* and Thaddeus Tarpey
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Companies:
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New York University School of Medicine and New York University
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Keywords:
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Precision Medicine;
Kullback-Leibler Divergence;
Prediction Model;
Mental Health
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Abstract:
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A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses are often similar across multiple treatments. Most precision medicine approaches using longitudinal data often ignore information from the longitudinal data structure. This talk investigates a powerful precision medicine approach by examining the impact of baseline covariates on longitudinal outcome trajectories to guide treatment decisions instead of traditional scalar outcome measures derived from longitudinal data. We introduce a method of estimating "biosignatures'' defined as linear combinations of baseline characteristics (i.e., a single index) that optimally separate longitudinal trajectories among different treatment groups. The criterion used is to maximize the Kullback-Leibler Divergences between different treatment outcome distributions. The approach is illustrated via simulation studies and a depression clinical trial. The approach is also contrasted with more traditional methods and compares performance in the presence of missing data.
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Authors who are presenting talks have a * after their name.
