The synergy factor: A statistic to measure interactions in complex diseases

被引：100

作者：

Cortina-Borja M. ^{[1
]}

Smith A.D. ^{[2
]}

Combarros O. ^{[3
,4
]}

Lehmann D.J. ^{[2
]}

机构：

[1] Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, London, WC1N 1EH

[2] Oxford Project to Investigate Memory and Ageing (OPTIMA), Department of Physiology, Anatomy and Genetics, Oxford, OX1 3QX, South Parks Road

[3] Neurology Service, Centro de Investigacián Biomédica en Red Sobre Enfermedades Neurodegenerativas, Sevilla

[4] Marqués de Valdecilla University Hospital, University of Cantabria

来源：

BMC Research Notes | / 2卷 / 1期

基金：

英国医学研究理事会;

关键词：

Binary Interaction; Accessible Method; Susceptibility Factor; High Order Interaction; Bootstrap Approximation;

D O I：

10.1186/1756-0500-2-105

中图分类号：

学科分类号：

摘要：

Background. 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. Findings. 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. Conclusion. 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. © 2009 Combarros et al; licensee BioMed Central Ltd.

引用

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