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首页> 美国卫生研究院文献>Oncotarget >Controlling for confounding factors and revealing their interactions in genetic association meta-analyses: a computing method and application for stratification analyses
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Controlling for confounding factors and revealing their interactions in genetic association meta-analyses: a computing method and application for stratification analyses

机译：控制混杂因素并揭示其在遗传关联荟萃分析中的相互作用：分层分析的一种计算方法和应用

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摘要

Subgroup and stratification analyses have been widely applied in genetic association studies to compare the effects of different factors or control for the effects of the confounding variables associated with a disease. However, studies have not systematically provided application standards and computing methods for stratification analyses. Based on the Mantel-Haenszel and Inverse-Variant approaches and two practical computing methods described in previous studies, we propose a standard stratification method for meta-analyses that contains two sequential steps: factorial stratification analysis and confounder-controlling stratification analysis. Examples of genetic association meta-analyses are used to illustrate these points. The standard stratification analysis method identifies interacting effects on investigated factors and controls for confounding variables, and this method effectively reveals the real effects of these factors and confounding variables on a disease in an overall study population. We also discuss important issues concerning stratification for meta-analyses, such as conceptual confusion between subgroup and stratification analyses, and incorrect calculations previously used for factorial stratification analyses. This standard stratification method will have extensive applications in future research for increasing studies on the complicated relationships between genetics and disease.
机译：亚组和分层分析已广泛应用于遗传关联研究中，以比较不同因素的影响或控制与疾病相关的混杂变量的影响。然而，研究尚未系统地提供用于分层分析的应用标准和计算方法。基于Mantel-Haenszel和Inverse-Variant方法以及先前研究中描述的两种实用计算方法，我们提出了一种用于荟萃分析的标准分层方法，该方法包括两个连续步骤：阶乘分层分析和混杂控制分层分析。遗传关联荟萃分析的例子用于说明这些观点。标准分层分析方法确定了对调查因素和混杂变量控制的相互作用影响，并且该方法有效地揭示了这些因素和混杂变量对整个研究人群中疾病的实际影响。我们还将讨论有关荟萃分析分层的重要问题，例如子组和分层分析之间的概念混淆，以及以前用于阶乘分层分析的错误计算。这种标准的分层方法将在未来的研究中广泛应用，以增加对遗传学和疾病之间复杂关系的研究。

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