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网络荟萃分析中多组份干预措施的建模。

Modeling Multicomponent Interventions in Network Meta-Analysis.

机构信息

Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.

Department of Surgery and Cancer, Faculty of Medicine, Institute of Reproductive and Developmental Biology, Imperial College, London, UK.

出版信息

Methods Mol Biol. 2022;2345:245-261. doi: 10.1007/978-1-0716-1566-9_15.

Abstract

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.

摘要

评估包含药物(多疗法)或多种成分的医疗干预措施的试验正在迅速增加。在后一种干预措施(也称为复杂干预措施)中,复杂性是至关重要的。例如,非药物干预措施,如心理干预或自我管理干预,通常具有共同的组成部分,这些组成部分与干预措施的性质、实施者、地点和方式有关。在试验网络中,通常需要确定最有效(或最安全)的组成部分和/或组成部分的组合。文献中提出了四种关键的荟萃分析方法来处理复杂干预措施。这些方法包括 (a) 单一效应模型,(b) 完全交互模型,(c) 加性主效应模型,和 (d) 双向交互模型。在本章中,我们介绍和讨论了这些方法的优缺点。我们使用评估 2 型糖尿病患者自我管理干预对腰围相对效果的网络来说明这些方法。

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