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Unveiling the Heterogeneity of Treatment Effects in Psychotherapy for Depression: A Synthesis of Individual Patient Data

CORDIS · observation · Publication date unknown

nd study contexts. SYNTHESIS addresses these challenges by utilizing an ambitious computational framework, which integrates Individual Patient Data (component) Network Meta-Analyses (IPD[c]NMAs) with machine learning (ML) to pave the way to precision psychology. It reconceptualizes psychotherapy as a dynamic, context-sensitive, and patient-centered network of interventions, addressing a key unanswered question: i.e., what is the optimal treatment, and its components based on individual differences. Moving beyond traditional approaches focusing on group averages, SYNTHESIS will uncover the HTE, addressing the generalizability crisis in psychotherapy research while advancing knowledge of treatment response variability. Key innovations include comparing all head-to-head comparison of psychotherapies, deconstructing these therapies into components, mapping their interactions with patient characteristics, and using ML to identify complex patterns in treatment responses within a causal IPD(c)NMA framework. Outputs such as an unparalleled IPD data warehouse, predictive models, and open-acce

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recordType

award

status

SIGNED

region

EU

value

2038241

unit

EUR