Greenland S
Department of Epidemiology, UCLA School of Public Health 90095-1772, USA.
Epidemiology. 1995 Jul;6(4):356-65. doi: 10.1097/00001648-199507000-00005.
Standard categorical analysis is based on an unrealistic model for dose-response and trends and does not make efficient use of within-category information. This paper describes two classes of simple alternatives that can be implemented with any regression software: fractional polynomial regression and spline regression. These methods are illustrated in a problem of estimating historical trends in human immunodeficiency virus incidence. Fractional polynomial and spline regression are especially valuable when important nonlinearities are anticipated and software for more general nonparametric regression approaches is not available.
标准分类分析基于剂量反应和趋势的不切实际模型，并且没有有效利用类别内信息。本文描述了两类简单的替代方法，它们可以用任何回归软件来实现：分数多项式回归和样条回归。这些方法在估计人类免疫缺陷病毒发病率的历史趋势问题中得到了说明。当预计存在重要的非线性且没有更通用的非参数回归方法的软件时，分数多项式回归和样条回归特别有价值。
