用因果模型分析抑郁症换药效果，发现加量未必更好。
Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings
- 构建未来就诊的反事实预测任务，用8种方法估算换药后抑郁评分
- 因果森林表现最优，发现特定换药方向疗效更显著，加量普遍有益
- 揭示部分患者低强度治疗反而更优，为临床决策提供可验证建议
抑郁症治疗常因疗效不佳或副作用需更换药物。在纵向换药场景中，个体化治疗效果估计面临多重挑战：治疗分配受患者特征混淆、换药引发时变选择偏倚，且随访数据中无法观测反事实结果。基于某专有纵向重度抑郁障碍（MDD）临床试验数据集，我们构建了下一诊次反事实预测任务，旨在估计不同治疗方案下的汉密尔顿抑郁量表（HAMD-17）总分。我们对比了8种估计器，包括元学习器、残差法及树模型。因果森林（CF）在所有评估指标下均表现最优且稳定。分析显示症状改善集中在特定换药路径，剂量强化总体有益。值得注意的是，存在反直觉现象：对特定患者亚群，低强度方案优于高强度替代方案。粗略观察比较夸大实际收益，而调整混杂后的估计值较小但具有可操作性。研究结果为抑郁症诊疗中的临床决策支持提供了前瞻性可检验的候选方案。
原文摘要 · Abstract (English)
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 approaches. Causal Forest (CF) demonstrates the most favorable and consistent performance across all criteria. Our analysis shows that symptom benefits concentrate in specific switch directions, with dose intensification being generally beneficial. Notably, we identify a counterintuitive exception where a lower-intensity regimen outperforms a higher-intensity alternative for specific patient subsets. While crude observational comparisons substantially overstate gains, confounding-adjusted estimates yield modest, actionable magnitudes. These findings provide prospectively testable candidates for clinical decision support in depression care.
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