In describing the impact of an intervention, a single effect size, odds ratio, or other summary measure is often employed. This single measure is useful in calibrating the effect of one intervention against others, but it is less meaningful when the intervention displays variation in impact. A single intervention trial can show differential effects when subgroups respond differentially, when impact varies by environmental context, or when there is varying impact with different outcome measures or across follow-up time. This article presents a multilevel mixture modeling approach for meta-analyses that summarizes these sources of impact variation across trials and measured outcomes.

中文翻译：

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检查环境如何改变干预影响：在多级混合元分析中使用效应量。

在描述干预的影响时，通常使用单一的效果大小、优势比或其他总结性措施。这种单一的衡量标准有助于校准一种干预措施对另一种干预措施的影响，但当干预措施显示出影响的变化时，它就没有意义了。当亚组反应不同时，当影响因环境背景而异，或者当不同结果测量或不同随访时间产生不同影响时，单个干预试验可以显示不同的效果。本文介绍了一种用于荟萃分析的多级混合建模方法，该方法总结了跨试验和测量结果的影响变化的这些来源。