Using Causal Inference and Machine Learning Methods to Predict Cognitive Behavioral Treatment Response

项目摘要/摘要 许多患有焦虑和恐惧障碍(AFD)的患者报告称，在接受以下证据治疗时，益处微乎其微- 基于心理治疗(例如，认知行为疗法[CBT])或药物疗法(例如，抗抑郁药物； 苯二氮卓类)。相反，一些AFD患者可能从几乎任何治疗中受益(例如， 支持性治疗)。使用在随机对照试验(RCT)中收集的数据，已经受到限制 确定如何利用治疗前特征将AFD患者与治疗相匹配的进展 最有可能带来好处的。因此，NIMH提出了专注于确定 治疗调节剂和预测不同治疗反应的开发工具。这样做的总体目标是 建议的二次数据分析是将因果推理和机器学习方法应用于预期 预测AFDs患者不同治疗反应的观察数据。样本(n= 1,528)来自NIMH资助的一项针对AFD患者的长期研究，这些患者接受了：(A)CBT合并 药物治疗，(B)不进行药物治疗的CBT，或(C)照常治疗(TAU)。目标最大值 似然估计(一种因果推理方法)和超级学习(一种集成机器学习方法) 将用于实现拟议的目标。目标1将估计这三个因素的(总体)平均影响 治疗类型。Aim 2将评估“最佳治疗规则”，以确定区别治疗反应 可以根据患者治疗前症状的多维特征进行有意义的预测。目标3 将评估“最佳治疗规则”，以确定区别治疗反应是否有意义 使用所有可用的治疗前协变量进行预测。这项拟议的研究具有很高的创新性，可以 显著影响越来越多的文献侧重于预测差异治疗效果和个性化 AFDs患者的治疗。尽管使用观测数据和数据获得的治疗效果估计 因果推断方法(即，根据非随机治疗选择进行调整)类似于 RCT，这将是第一次将这种方法应用于AFD患者数据的研究。这项研究也将是第一次 利用集成机器学习来开发用于AFD的复合调节剂(即最佳处理规则)。在……里面 相比之下，以前开发复合主持人的尝试依赖于灵活性较差的模型构建 程序容易过度拟合并且不能捕获复杂的预测器-结果关联(例如， 预测者之间的相互作用；非线性关联)。在实现拟议目标方面，目前的研究 将是未来研究的催化剂，使用因果推理和机器学习来研究 AFD患者的不同治疗反应。结果将被用来证明未来旨在 在更大的观测样本和实用RCT(例如，最优)中扩展和验证模型 特定药物/剂量的治疗规则；相对于CBT的药物治疗时机；第二波CBT 与基于接受的CBT相比)。

PROJECT SUMMARY/ABSTRACT Many patients with anxiety and fear disorders (AFDs) report minimal benefits when treated with an evidence- based psychotherapy (e.g., cognitive-behavioral therapy [CBT]) or pharmacotherapy (e.g., antidepressants; benzodiazepines). Conversely, some AFD patients are likely to benefit from virtually any treatment (e.g., supportive therapy). Using data collected in randomized controlled trials (RCTs), there has been limited progress determining how to use pre-treatment characteristics to match AFD patients to the treatment that is most likely to provide benefit. As a result, NIMH has forwarded Strategic Objectives focused on identifying treatment moderators and developing tools that predict differential treatment response. The broad goal of this proposed secondary data analysis is to apply causal inference and machine learning methods to prospective observational data to predict differential treatment response among patients with AFDs. The sample (n = 1,528) is from a longstanding NIMH-funded study of AFD patients who received: (a) CBT with concurrent pharmacotherapy, (b) CBT without pharmacotherapy, or (c) treatment as usual (TAU). Targeted maximum likelihood estimation (a causal inference method) and super learning (an ensemble machine learning method) will be used to accomplish the proposed Aims. Aim 1 will estimate the (overall) average effects of the three treatment types. Aim 2 will estimate “optimal treatment rules” to determine if differential treatment response can be meaningfully predicted based on a patient's multidimensional profile of pre-treatment symptoms. Aim 3 will estimate “optimal treatment rules” to determine if differential treatment response can be meaningfully predicted using all available pre-treatment covariates. The proposed study is highly innovative and could significantly impact the growing literature focused on predicting differential treatment effects and personalizing treatment for patients with AFDs. Although treatment effects estimates obtained using observational data and causal inference methods (i.e., adjusted for nonrandom treatment selection) are similar to those estimated in RCTs, this would be the first study to apply such methods to AFD patient data. This study will also be the first to use ensemble machine learning to develop composite moderators for AFDs (i.e., optimal treatment rules). In comparison, prior attempts to develop composite moderators have relied on less flexible model-building procedures prone to overfitting and unable to capture complex predictor-outcome associations (e.g., interactions among predictors; nonlinear associations). In achieving the proposed Aims, the current study would be a catalyst for future research using causal inference and machine learning to study predictors of differential treatment response among AFD patients. Results will be used to justify future research aimed at expanding and validating the models in larger observational samples and pragmatic RCTs (e.g., optimal treatment rules for specific medications/doses; timing of pharmacotherapy relative to CBT; second-wave CBT versus acceptance-based CBT).