Learning optimal biomarker-guided treatment policy for chronic disorders

Document Type

Article

Publication Date

6-30-2024

Abstract

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

Identifier

85192176079 (Scopus)

Publication Title

Statistics in Medicine

External Full Text Location

https://doi.org/10.1002/sim.10099

e-ISSN

10970258

ISSN

02776715

PubMed ID

38700103

First Page

2765

Last Page

2782

Issue

14

Volume

43

Grant

MH123487

Fund Ref

National Institutes of Health

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