Lewis Fraser I, Ward Michael P
Section of Epidemiology, VetSuisse Faculty, University of Zürich, Winterthurerstrasse 270, Zürich, CH 8057, Switzerland.
Emerg Themes Epidemiol. 2013 May 17;10(1):4. doi: 10.1186/1742-7622-10-4.
: Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression - a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) - has long been the standard model. Generalizing multivariable regression to multivariate regression - all variables potentially statistically dependent - offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established - Bayesian network structure discovery - and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease processes at the population level, leading to the design of better disease control and prevention programs.
回归建模是流行病学分析中应用最广泛的方法之一。它提供了一种识别统计关联的方法，据此可进一步研究与疾病控制相关的潜在因果关联。多变量回归（一个因变量（结果，通常为疾病）和多个自变量（预测因素））长期以来一直是标准模型。将多变量回归推广到多因素回归（所有变量都可能存在统计依存关系）可提供一个丰富得多的建模框架。通过一系列简单的示例，我们对这些方法进行了比较和对比。用于实施多因素回归的技术方法（贝叶斯网络结构发现）已经确立，虽然在流行病学文献中相对较新，但在计算机科学领域有着悠久的历史。多因素分析在流行病学研究中的应用有助于在人群层面更好地理解疾病过程，从而设计出更有效的疾病控制和预防方案。
