Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Adv Genet. 2010;72:25-45. doi: 10.1016/B978-0-12-380862-2.00002-3.
Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature.
逻辑回归是一种自适应分类和回归程序，最初是为了在遗传关联研究中揭示相互作用的单核苷酸多态性 (SNP) 而开发的。一般来说，当这些协变量的相互作用是主要关注点时，这种方法可以用于任何具有二进制预测器的设置中。逻辑回归搜索最佳解释因变量变化的二进制变量的布尔（逻辑）组合，从而揭示与响应相关且/或具有预测能力的变量和相互作用。逻辑表达式嵌入在广义线性回归框架中，因此逻辑回归可以处理各种类型的结果，例如病例对照研究中的二元响应、数值响应和生存数据。在本章中，我们将介绍逻辑回归方法学，列出公共卫生和医学中的一些应用，并总结文献中提出的逻辑回归的一些直接扩展和修改。
