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.
Keywords: Large data; methodology; prediction; treatment trials.
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