Individual participant data network meta-analysis of psychosocial interventions for survivors of intimate partner violence: Study protocol
- PMID: 40100901
- PMCID: PMC11957554
- DOI: 10.1371/journal.pone.0306669
Individual participant data network meta-analysis of psychosocial interventions for survivors of intimate partner violence: Study protocol
Abstract
Many systematic reviews and meta-analyses have been conducted in the field of Intimate Partner Violence (IPV) and the evidence shows small to moderate effect sizes in improving mental health outcomes. However, there is considerable heterogeneity due to variation in participants, interventions and contexts. It is therefore important to establish which participant and intervention characteristics affect the different psychosocial outcomes in different contexts. Individual Participant Network Meta-analysis (IPDNMA) is a gold-standard method to estimate moderating effects, compare the effectiveness of different interventions and thus answer the question of which intervention is best-suited for whom. We will conduct an IPDNMA of randomised controlled trials (RCTs) of psychosocial interventions for IPV survivors aimed at improving mental health, psychosocial outcomes such as self-efficacy and quality of life, reducing IPV and increasing safety-behaviours and dropout from the intervention (as an indication of intervention acceptability) compared to any type of control (PROSPERO registration number: CRD42023488502). We aim to establish collaborations with the authors of eligible RCTs, to obtain and harmonise the Individual Participant Data of the trials. We will conduct one-stage IPDNMA under a Bayesian framework using the multinma package in R, after testing which characteristics of the participants and interventions are effect modifiers. We anticipate that not all study authors will provide access to IPD, which is a limitation of IPDNMA. We aim to address this by combining studies with aggregate data and studies with IPD using Multi-Level Network Meta-Regression (ML-NMR) implemented in the multinma R package. This approach is novel in the field and makes full use of available evidence to inform clinical and policy-related decision making.
Copyright: © 2025 Palantza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Conflict of interest statement
I have read the journal's policy and the authors of this manuscript have the following competing interests: Christina Palantza is in receipt of an annual stipend from the University of Bristol from 2023 to 2027. This does not alter our adherence to PLOS ONE policies on sharing data and materials. CP’s scholarship granted by the University of Bristol does not constitute a competing interest. Role of funder: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Non-Financial competing interest: None.
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