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Activity Number: 322 - Novel Statistical Methods and Applications in Precision Mental Health
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #320854
Title: Precision Health Treatment Decision Rules Optimization in Mental Health Using Longitudinal Outcome Trajectories
Author(s): Thaddeus Tarpey* and Lanqiu Yao
Companies: New York University and New York University School of Medicine
Keywords: functional data analysis; Kullback-Leibler divergence

Personalizing treatment decisions for individual patients is a major component of precision medicine. Treatment decision rules (TDRs) achieve this objective by mapping a patient's baseline characteristics to a set of treatment options. Most studies that generate data for constructing TDRs are longitudinal in nature, but most methodological work on developing optimal TDRs ignore the longitudinal information available in the data and use instead simple summary measures for the outcome (e.g., a change score). In this talk, we propose a methods of constructing TDRs that take the longitudinal nature of the data into consideration and show that these approaches are more powerful than TDR methods that ignore longitudinal information, especially when missing data is a consideration. The approaches are illustrated using data from a depression clinical trial.

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

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