Department of Medical Epidemiology, Karolinska Institute, Stockholm, Sweden,
Eur J Epidemiol. 2014 Nov;29(11):813-20. doi: 10.1007/s10654-014-9953-9. Epub 2014 Sep 24.
A common goal of epidemiologic research is to study how two exposures interact in causing a binary outcome. Sufficient-cause interaction is a special type of mechanistic interaction, which requires that two events (e.g. specific exposure levels from two risk factors) are necessary in order for the outcome to occur. Recently, tests have been derived to establish the presence of sufficient-cause interactions, for categorical exposures with at most three levels. In this paper we derive prevalence bounds, i.e. lower and upper bounds on the prevalence of subjects for which sufficient-cause interaction is present. The derived bounds hold for categorical exposures with arbitrary many levels. We apply the bounds to data from a study of gene-gene interaction in the development of Rheumatoid Arthritis. We provide an R-program to estimate the bounds from real data .
流行病学研究的一个共同目标是研究两种暴露因素如何相互作用导致二项结局。充分病因相互作用是一种特殊类型的机制相互作用，它要求两个事件（例如，来自两个危险因素的特定暴露水平）必须发生，结果才能发生。最近，已经开发出了用于确定存在充分病因相互作用的检验方法，适用于最多有三个水平的分类暴露。在本文中，我们推导出了流行率界限，即存在充分病因相互作用的受试者的流行率的下限和上限。推导出的界限适用于具有任意多个水平的分类暴露。我们将界限应用于类风湿关节炎发病中基因-基因相互作用研究的数据。我们提供了一个 R 程序来从实际数据中估计界限。
