预防研究中多水平有序数据的方法。
Methods for Multilevel Ordinal Data in Prevention Research.
作者信息
Hedeker Donald
机构信息
Division of Epidemiology and Biostatistics (M/C 923), School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Room 955, Chicago, IL, 60612-4336, USA.
出版信息
Prev Sci. 2015 Oct;16(7):997-1006. doi: 10.1007/s11121-014-0495-x.
Abstract
This paper discusses statistical models for multilevel ordinal data that may be more appropriate for prevention outcomes than models that assume continuous measurement and normality. Prevention outcomes often have distributions that make them inappropriate for many popular statistical models that assume normality and are more appropriately considered ordinal outcomes. Despite this, the modeling of ordinal outcomes is often not well understood. This article discusses ways to analyze multilevel ordinal outcomes that are clustered or longitudinal, including the proportional odds regression model for ordinal outcomes, which assumes that the covariate effects are the same across the levels of the ordinal outcome. The article will cover how to test this assumption and what to do if it is violated. It will also discuss application of these models using computer software programs.
摘要
本文讨论了用于多级有序数据的统计模型，这些模型可能比假设连续测量和正态性的模型更适合预防结果。预防结果的分布往往使其不适用于许多假设正态性的流行统计模型，而更适合被视为有序结果。尽管如此，有序结果的建模往往没有得到很好的理解。本文讨论了分析聚类或纵向多级有序结果的方法，包括有序结果的比例优势回归模型，该模型假设协变量效应在有序结果的各个水平上是相同的。本文将介绍如何检验这一假设以及如果假设被违反该如何处理。还将讨论使用计算机软件程序应用这些模型的情况。
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