Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings
Depression treatment often requires switching medications due to inadequate response or adverse effects. Estimating individualized treatment effects in this setting is challenging because treatment assignment is confounded by patient characteristics, switching induces time-varying selection, and counterfactual outcomes are not observed in follow-up data. Using a proprietary longitudinal major depressive disorder (MDD) clinical trial dataset, we formulate a next-visit counterfactual prediction task to estimate Hamilton Depression Rating Scale (HAMD-17) total scores under alternative treatments. We benchmark 8 estimators, including meta-learners, residual-based methods, and tree-based appro...
| Authors | Xinyu Qin, Martin Katzman, Alexandria Greifenberger et al. |
|---|---|
| Primary category | stat.AP |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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