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亲密伴侣暴力幸存者心理社会干预的个体参与者数据网络荟萃分析:研究方案。

Individual participant data network meta-analysis of psychosocial interventions for survivors of intimate partner violence: Study protocol.

作者信息

Palantza Christina, Morgan Karen, Welton Nicky J, Micklitz Hannah M, Sander Lasse B, Feder Gene

机构信息

Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

Medical Psychology and Medical Sociology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

出版信息

PLoS One. 2025 Mar 18;20(3):e0306669. doi: 10.1371/journal.pone.0306669. eCollection 2025.

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.

摘要

在亲密伴侣暴力(IPV)领域已经进行了许多系统评价和荟萃分析,证据表明在改善心理健康结果方面的效应大小为小到中等。然而,由于参与者、干预措施和背景的差异,存在相当大的异质性。因此,确定哪些参与者和干预特征在不同背景下影响不同的心理社会结果非常重要。个体参与者网络荟萃分析(IPDNMA)是一种估计调节效应、比较不同干预措施有效性的金标准方法,从而回答哪种干预措施最适合谁的问题。我们将对针对IPV幸存者的心理社会干预随机对照试验(RCT)进行IPDNMA,旨在改善心理健康、自我效能感和生活质量等心理社会结果,减少IPV,增加安全行为,并与任何类型的对照相比,降低干预的退出率(作为干预可接受性的指标)(PROSPERO注册号:CRD42023488502)。我们的目标是与符合条件的RCT作者建立合作,以获取并协调试验的个体参与者数据。在测试参与者和干预措施的哪些特征是效应修饰因素后,我们将使用R语言中的multinma包在贝叶斯框架下进行单阶段IPDNMA。我们预计并非所有研究作者都会提供个体参与者数据访问权限,这是IPDNMA的一个局限性。我们旨在通过使用multinma R包中实现的多水平网络荟萃回归(ML-NMR)将汇总数据研究与个体参与者数据研究相结合来解决这一问题。这种方法在该领域是新颖的,并充分利用现有证据为临床和政策相关决策提供信息。

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本文引用的文献

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Developmental Consequences of Intimate Partner Violence on Children.亲密伴侣暴力对儿童发展的影响。
Annu Rev Clin Psychol. 2023 May 9;19:303-329. doi: 10.1146/annurev-clinpsy-072720-013634. Epub 2023 Feb 15.
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Using individual participant data to improve network meta-analysis projects.利用个体参与者数据改进网络荟萃分析项目。
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