Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0717, USA.
Dose Response. 2006 May 22;3(4):474-90. doi: 10.2203/dose-response.003.04.004.
Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationships. The examples presented reveal that no single approach is universally appropriate; rather, these (and possibly other) nonlinearities require for their discovery a variety of both graphical and numeric techniques.
非线性剂量反应关系在其发现方面带来了统计学挑战。即使最初采用线性近似，然后采用其他方法，结果也可能具有误导性，甚至完全排除正在研究的非线性关系的发现。我们回顾了用于检测非线性关系的各种直接的统计方法，并讨论了阻碍其检测的几个因素。我们的具体背景是暴露-结果关联的流行病学研究，我们重点关注阈值和 J 效应剂量反应关系。所呈现的示例表明，没有一种方法是普遍适用的；相反，这些（和可能其他的）非线性关系需要使用各种图形和数值技术来发现它们。
