Lin Shuhuang, Liu Xu, Yao Bin, Huang Zunnan
Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University, Dongguan, Guangdong 523808, China.
The Second School of Clinical Medicine, Guangdong Medical University, Dongguan, Guangdong 523808, China.
Oncotarget. 2018 Jan 29;9(15):12125-12136. doi: 10.18632/oncotarget.24335. eCollection 2018 Feb 23.
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.
亚组分析和分层分析已广泛应用于基因关联研究，以比较不同因素的作用或控制与疾病相关的混杂变量的影响。然而，此前的研究尚未系统地提供分层分析的应用标准和计算方法。基于Mantel-Haenszel法和逆方差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：析因分层分析和控制混杂因素的分层分析。我们通过基因关联荟萃分析的实例来说明这些要点。标准分层分析方法能够识别对研究因素的交互作用并控制混杂变量，并且该方法能在整体研究人群中有效揭示这些因素和混杂变量对疾病的实际影响。我们还讨论了荟萃分析分层的重要问题，如亚组分析和分层分析之间的概念混淆，以及先前用于析因分层分析的错误计算。这种标准分层方法将在未来研究中得到广泛应用，以增加对基因与疾病复杂关系的研究。
