To improve outcomes for youth who do not respond optimally to existing treatments, we need to identify robust predictors, moderators, and mediators that are ideal targets for personalisation in mental health care. We propose a solution to leverage the Individual Patient Data Meta‐analysis (IPDMA) approach to allow broader access to individual‐level data while maintaining methodological rigour. Such a resource has the potential to answer questions that are unable to be addressed by single studies, reduce researcher burden, and enable the application of newer statistical techniques, all to provide data‐driven strategies for clinical decision‐making. Using childhood anxiety as the worked example, the editorial perspective outlines the rationale for leveraging IPDMA methodology to build a data repository, the Platform for Anxiety Disorder Data in Youth. We also include recommendations to address the methods and challenges inherent in this endeavour.

中文翻译：

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编辑观点：扩展 IPDMA 方法以推动儿童心理健康的治疗个性化


为了改善对现有治疗反应不佳的青少年的预后，我们需要确定稳健的预测因子、调节因子和中介因子，它们是心理健康护理个性化的理想目标。我们提出了一种解决方案，利用个体患者数据荟萃分析 （IPDMA） 方法，允许更广泛地访问个体层面的数据，同时保持方法的严谨性。这样的资源有可能回答单个研究无法解决的问题，减轻研究人员的负担，并支持应用更新的统计技术，所有这些都为临床决策提供数据驱动的策略。以童年焦虑为例，编辑视角概述了利用 IPDMA 方法构建数据存储库的基本原理，即青少年焦虑症数据平台。我们还提出了一些建议，以解决这项工作中固有的方法和挑战。