What works for Whom in Pediatric OCD

项目摘要/摘要 通过理解“什么是有效的，对谁有效？”可以改善患者的预后。实现更好的 将患者与可用的治疗相匹配。尽管有大量研究寻求优化CBT结果 对于儿童强迫症(OCD)，我们对如何匹配的了解有限 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，可能 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，结果一直不一致，只关注一种修饰物 时间长，难以整合不同样本特性的试验结果。审判间歇 样品特性的差异严重限制了传统的元分析工具，极大地复杂化了 治疗效果的交叉试验比较。该项目将使用来自27个试验的个人参与者数据和 来自两个临床人群的数据，并利用可携带性方法的最新进展，有助于 理解“什么对谁管用？”可运输性方法包括因果分析方法 当分布有显著差异时，将推论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 处理试验间协变量的差异，使试验中的因果估计得以传递 将样本转移到目标人群。在明确的因果和统计假设下，传输的分析 对干预措施在目标人群中的公平性提供公正的估计(S)，使双方能够平分秋色 干预措施的比较，即使在两种干预措施没有在面对面试验中进行比较的情况下也是如此。在……里面 此外，目标人群可以被定义为亚人群(例如，患有高疾病的较小儿童 严重性、合并症)，从而能够有效地评估针对该亚人群的相关干预措施 提供对“什么对谁有效？”这一问题的见解。

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?”