What works for Whom in Pediatric OCD

项目总结/摘要 通过了解“什么对谁有效”，将提高患者的治疗效果。实现更好 将患者与现有治疗方法相匹配。尽管有大量的研究试图优化CBT的结果， 对于儿童强迫症（OCD），我们对如何匹配 为患者提供适当的治疗方案。过去的研究已经确定了患者的特征， 影响治疗反应，包括年龄、症状严重程度、合并症和既往治疗史。 不幸的是，由于样本量有限，研究结果并不一致， 时间，以及难以整合具有不同样本特征的试验结果。试验间 样本特征的差异严重限制了传统的荟萃分析工具， 治疗效果的交叉试验比较。该项目将使用来自27项试验的个体参与者数据， 来自两个临床人群的数据，并利用可移植性方法的最新进展， 理解“什么对谁有效？”可运输性方法包括因果分析方法， 当分布存在显著差异时，将推断从试验样本扩展到目标人群 试验和目标人群之间的样本特征。使用稳健的统计模型 解决协变量的试验间差异，使因果估计从试验 目标人群的样本。在明确的因果关系和统计假设下， 提供关于干预措施在目标人群中如何公平的无偏估计， 即使在头对头试验中没有对两种干预措施进行比较，也需要对干预措施进行比较。在 此外，目标群体可以被定义为子群体（例如，幼儿高发病 严重程度、合并症），从而能够有效地评估该亚群的相关干预措施 深入探讨“什么对谁有效”的问题

Project Summary/Abstract Improving patient outcomes will be enhanced by understanding “what works, for whom?” enabling better matching of patients to available treatments. Despite a wealth of research seeking to optimize CBT outcomes for pediatric obsessive-compulsive disorder (OCD), we have limited understanding about how to match patients with the appropriate treatment package. Past research has identified patient characteristics that may influence treatment response, including age, symptom severity, comorbidities, and previous treatment history. Unfortunately, findings have been inconsistent due to limited sample size, a focus on one modifier at a time, and difficulty integrating results across trials with different sample characteristics. Between-trial differences in sample characteristics severely limit conventional meta-analytic tools, greatly complicating cross-trial comparisons of treatment efficacy. This project will use individual-participant-data from 27 trials and data from two clinical populations and utilize recent advancements in transportability methods facilitates understanding “what works, for whom?” Transportability methods include causal analytic approaches to extend inferences from trial samples to target populations when there are marked differences in the distribution of sample characteristics between trials and the target population. Robust statistical models are used to address between-trial differences in covariates, enabling the transportation of causal estimates from trial samples to target populations. Under explicit causal and statistical assumptions, the transported analyses provide unbiased estimates of how interventions will fair in the target population(s), enabling apples-to-apples comparisons of interventions even when two interventions have not been compared in a head-to-head trial. In addition, the target population can be defined as sub-populations (e.g., younger children with high illness severity, comorbidities), enabling evaluation of relevant interventions for that sub-population, effectively providing insight into the question of “what works for whom?”