Work

Estimating optimal dynamic treatment regime with model selection in STAR*D trial

Year: 2025

Type: data-paper

Abstract: This NDA Study is linked to the paper "Model selection with uncertainty in estimating optimal dynamic treatment regimes" under preparation for statistical journal submission. We applied our model selection methodology to STAR*D to learn/estimate the optimal dynamic treatment regime for patients who did not achieve remission from initial treatment. We restrict our attention to two treatment options, augmentation and switch, for each decision. The original STAR*D data consists of four levels where all patients were treated with citalopram at level 1 and only switch options were available for patients involved in level 4. We therefore only focus on level 2 (level 2 plus level 2A if level 2A available) and level 3. The decision rules considered at the two stages (i.e. level 2 and level 3) are restricted to interpretable ones. Specifically, we consider linear and tree-based rules.

Source: National Institutes of Health, National Institute of Mental Health (NIMH) Data Archive Repository

DOI: 10.15154/zazg-8261

Authors: Tom, Brian, Tom, Brian

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7077054592

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