Statistical assessment of ordinal outcomes in comparative studies
Statistical assessment of ordinal outcomes in comparative studies
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DOI：
10.1016/s0895-4356(96)00312-5
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发表时间：
1997-01-01
影响因子：
7.2
通讯作者：
Mayo, NE
中科院分区：
文献类型：
--
作者：
Scott, SC;Goldberg, MS;Mayo, NE
有序回归是一种相对较新的统计方法，用于分析排名结果。在过去，等级量表的分析往往没有充分利用数据的有序性，或者，通过分配任意的数字得分的等级。虽然有序回归模型现在可以充分利用排名数据，但它们并没有被广泛使用。这篇文章，针对临床研究人员和流行病学家，提供了这些方法的属性的描述。在青少年特发性脊柱侧凸的随访研究中使用顺序测量背痛，我们说明了这些方法的优点，并描述了如何解释估计的参数。与二进制逻辑回归的比较表明，为什么一个单一的二分法的顺序规模可能会导致不正确的推论。两个有序模型（比例优势和延续比模型）进行了讨论，并检查这些模型的拟合优度。我们的结论是，有序回归是一种功能强大，使用简单的工具，并产生一个可解释的参数，总结了各组之间的效果在所有水平的结果。版权所有（C）1997 Elsevier Science Inc.
Ordinal regression is a relatively new statistical method developed for analyzing ranked outcomes. In the past, ranked scales have often been analyzed without making full use of the ordinality of the data or, alternatively, by assigning arbitrary numerical scores to the ranks. While ordinal regression models are now available to make full use of ranked data, they are not used widely. This article, directed to clinical researchers and epidemiologists, provides a description of the properties of these methods. Using ordinal measures of back pain in a follow-up study of adolescent idiopathic scoliosis, we illustrate the advantages of these methods and describe how to interpret the estimated parameters. Comparisons with binary logistic regression are made to show why a single dichotomization of the ordinal scale may lead to incorrect inferences. Two ordinal models (the proportional odds and the continuation ratio models) are discussed, and the goodness-of-fit of these models is examined. We conclude that ordinal regression is a tool that is powerful, simple to use, and produces an interpretable parameter that summarizes the effect between groups over all levels of the outcome. Copyright (C) 1997 Elsevier Science Inc.