纵向治疗切换场景下抑郁症的治疗效应估计
Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings
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中文总结 AI 辅助
本研究针对纵向抑郁症治疗切换场景，使用MDD临床试验数据，通过因果森林等8种估计器估计个体化治疗效应，发现剂量增加通常有益但存在反直觉例外，为抑郁症临床决策提供候选方案。
中文摘要 AI 辅助
抑郁症治疗常因应答不足或不良反应需要换药。在此场景下估计个体化治疗效应颇具挑战，因为治疗分配受患者特征混杂，换药会引发时变选择，且随访数据中无法观测反事实结局。我们使用专有纵向重度抑郁症（MDD）临床试验数据集，构建下一次就诊的反事实预测任务，以估计不同治疗方案下汉密尔顿抑郁量表17项（HAMD-17）总分。我们对8种估计器进行基准测试，包括元学习器、基于残差的方法和基于树的方法。因果森林（CF）在所有标准下表现最优且最稳定。分析显示，症状获益集中在特定换药方向，剂量增加通常有益；值得注意的是，我们发现一个反直觉例外：针对特定患者亚组，低强度方案优于高强度替代方案。粗略的观察性比较会大幅夸大获益，经混杂调整的估计值则产生适度、可操作的量级。这些发现为抑郁症护理的临床决策支持提供了可前瞻性验证的候选方案。
英文摘要
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.
发表机构
- University of Houston（休斯顿大学）
- Adler Graduate Professional School（阿德勒研究生专业学校）
- Lakehead University（湖首大学）
- Northern Ontario School of Medicine（北安大略医学院）
- Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。
