Full search strategy.

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.

亲密伴侣暴力（Intimate Partner Violence, IPV）领域已开展大量系统评价与荟萃分析，现有证据显示，相关干预措施在改善心理健康结局方面的效应量处于小到中等水平。但由于研究对象、干预方案及研究场景存在差异，相关研究存在显著异质性。因此，明确在不同场景下哪些研究对象与干预特征会对不同社会心理结局产生影响，具有重要学术与实践价值。个体参与者数据网络荟萃分析（Individual Participant Network Meta-analysis, IPDNMA）是估算调节效应、比较不同干预措施有效性的金标准方法，可据此解答“何种干预方案最适配某类研究对象”这一核心问题。本研究将针对亲密伴侣暴力幸存者的社会心理干预开展一项个体参与者数据网络荟萃分析，纳入以改善心理健康、提升自我效能与生活质量等社会心理结局、减少亲密伴侣暴力发生、增加安全行为为目标的随机对照试验（Randomised Controlled Trials, RCTs），并以干预脱落率作为干预可接受性的评估指标，同时设置任意类型的对照组（PROSPERO注册号：CRD42023488502）。本研究计划与符合纳入标准的随机对照试验的作者建立合作，获取并统一整理各试验的个体参与者数据。在明确研究对象与干预措施的哪些特征属于效应调节因子后，本研究将采用R语言multinma包，在贝叶斯框架下开展单阶段个体参与者数据网络荟萃分析。我们预计并非所有研究作者都会提供个体参与者数据，这也是个体参与者数据网络荟萃分析的局限性之一。对此，本研究计划采用R语言multinma包实现的多层网络元回归（Multi-Level Network Meta-Regression, ML-NMR）方法，整合汇总数据研究与个体参与者数据研究。该方法在本领域尚属首创，可充分利用现有证据为临床与政策相关决策提供科学依据。