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使用来自多项试验的个人参与者数据来识别子组:在腰部疼痛中的应用

Cynthia Huber¹, Tim Friede¹

¹Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Lower Saxony, Germany.

Research synthesis methods

|February 2, 2026

概括

基于模型的递归分区 (MOB) 和metaMOB识别了受益于治疗的患者子组. 探索个体参与者数据元分析中的异质性对于腰部疼痛个性化医学至关重要.

科学领域:

生物统计学生物统计学
临床试验分析
个性化医疗是个性化的医疗.

背景情况:

基于模型的递归分区 (MOB) 和元MOB是用于识别具有差异治疗效果的患者子组的统计工具.
从多个试验中利用个体参与者数据 (IPD) 对于检测小治疗效益和识别响应者至关重要,特别是在昂贵的大规模研究中.
个体间治疗效果的异质性需要先进的分析方法.

研究的目的:

在非特异性腰部疼痛的背景下,应用MOB和metaMOB来识别具有差异性治疗效果的子组.
在IPD元分析中探索截取和治疗效果异质性的影响.
证明这些方法对个性化治疗策略的有用性.

主要方法:

使用基于模型的递归分区 (MOB) 和它的扩展,metaMOB.
在元分析中使用随机效应来建模治疗效应异质性.
将方法应用于从Patel等人的子集中获得的合成数据. 对腰部疼痛的IPD元分析.
在截取和治疗效应方面研究了异质性.

主要成果:

该研究成功地应用了MOB和metaMOB来识别具有差异治疗效果的潜在子组.
分析强调了考虑基线特征 (截取) 和治疗反应的异质性的重要性.
研究结果表明,这些方法可以揭示不同患者群体内的细微治疗效果.

结论:

在IPD元分析中,MOB和metaMOB是剖析异质性的有价值工具.
探索截取和治疗效果的异质性对于确定从干预中获益最多的患者子组至关重要.
这些方法支持开发个性化治疗策略,特别是对于腰部疼痛等疾病.

关键词:

在 GLMM-树.异质性的异质性个人参与者数据 (IPD)进行元分析.这种物质是metaMOB.基于模型的递归分区 (MOB).小组的标识子组的标识

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