The synergy factor: a statistic to measure interactions in complex diseases.
The synergy factor: a statistic to measure interactions in complex diseases.
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DOI：
10.1186/1756-0500-2-105
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发表时间：
2009-06-15
影响因子：
1.8
通讯作者：
Lehmann DJ
中科院分区：
文献类型：
--
作者：
Cortina-Borja M;Smith AD;Combarros O;Lehmann DJ
理解复杂疾病的一个挑战在于揭示易感因素之间的相互作用，如遗传多态性和环境暴露。因此，有必要明确审查这种相互作用。一个必然的结果是需要一个方便的方法来测量相互作用的大小和意义，这可以由非统计学家使用，并与总结，如公布的数据。由于缺乏这种现成的方法，导致该领域的混乱。协同因子（SF）允许在病例对照研究中评估二元相互作用。在本文中，我们描述了它的属性和它的新特性，例如，在计算功率检测协同效应，并在其应用荟萃分析。我们用阿尔茨海默病中的真实的例子来说明这些功能，例如BACE 1多态性和APOE 4之间潜在相互作用的荟萃分析：SF = 2.5，95%置信区间：1.5-4.2; p = 0.0001。协同因子易于使用和解释。可以通过本文中提供的Excel程序进行计算。与逻辑回归分析不同，该方法可以应用于任何大小的数据集，无论多么小。它可以应用于原始或汇总数据，例如已发表的数据。它可以与任何类型的敏感性因子一起使用，只要数据是二分法的。新功能包括功效估计和荟萃分析。
One challenge in understanding complex diseases lies in revealing the interactions between susceptibility factors, such as genetic polymorphisms and environmental exposures. There is thus a need to examine such interactions explicitly. A corollary is the need for an accessible method of measuring both the size and the significance of interactions, which can be used by non-statisticians and with summarised, e.g. published data. The lack of such a readily available method has contributed to confusion in the field. The synergy factor (SF) allows assessment of binary interactions in case-control studies. In this paper we describe its properties and its novel characteristics, e.g. in calculating the power to detect a synergistic effect and in its application to meta-analyses. We illustrate these functions with real examples in Alzheimer's disease, e.g. a meta-analysis of the potential interaction between a BACE1 polymorphism and APOE4: SF = 2.5, 95% confidence interval: 1.5–4.2; p = 0.0001. Synergy factors are easy to use and clear to interpret. Calculations may be performed through the Excel programmes provided within this article. Unlike logistic regression analysis, the method can be applied to datasets of any size, however small. It can be applied to primary or summarised data, e.g. published data. It can be used with any type of susceptibility factor, provided the data are dichotomised. Novel features include power estimation and meta-analysis.