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个体治疗4:荟萃分析能否帮助针对最可能受益的个体进行干预?

Treating individuals 4: can meta-analysis help target interventions at individuals most likely to benefit?

作者信息

Thompson Simon G, Higgins Julian P T

机构信息

MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2 sR, UK.

出版信息

Lancet. 2005;365(9456):341-6. doi: 10.1016/S0140-6736(05)17790-3.

Abstract

Meta-analyses of randomised trials aim to summarise the effects of interventions across many patients, and can seem remote from the clinical issue of how individual patients should be treated and which patient groups will benefit the most from treatment. One method that attempts to address this point entails relating the overall effect in every trial to summaries of patient characteristics. This is called meta-regression. The interpretation of such analyses is not straightforward, however, because of a combination of confounding and other biases. Much more useful is to compare the outcomes for patient subgroups within trials and combine these results across trials. Unfortunately this method is rarely possible using published information, so analyses of individual patient data from trials are necessary. Also, although meta-analyses generally summarise an intervention's effect as a relative risk reduction, the groups of patients with the greatest absolute risk reduction have the most to gain.

摘要

随机试验的荟萃分析旨在总结众多患者中干预措施的效果,这似乎与如何治疗个体患者以及哪些患者群体将从治疗中获益最多这一临床问题相去甚远。一种试图解决这一问题的方法是将每个试验中的总体效应与患者特征总结联系起来。这被称为元回归。然而,由于混杂因素和其他偏倚的综合作用,此类分析的解释并不简单。更有用的是比较试验中患者亚组的结果,并将这些结果在不同试验中进行合并。不幸的是,使用已发表的信息很少能做到这一点,因此有必要对试验中的个体患者数据进行分析。此外,尽管荟萃分析通常将干预措施的效果总结为相对风险降低,但绝对风险降低最大的患者群体获益最多。

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