Ordinal Logistic Regression
Ordinal Logistic Regression
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DOI：
10.1007/978-3-319-19425-7_13
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发表时间：
2015-01-01
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影响因子：
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通讯作者：
Harrell, Frank E., Jr.
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文献类型：
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作者：
Harrell, Frank E., Jr.
许多医学和流行病学研究纳入了有序反应变量。在某些情况下，顺序响应Y代表标准测量量表的水平，例如疼痛的严重程度（无、轻度、中度、重度）。在其他情况下，通过指定单独端点的层次结构来构造有序响应。例如，临床医生可以指定几个组成事件的严重程度的排序，并将患者分配到无、心脏病发作、致残性中风和死亡中存在的最严重事件。有序响应方法的另一个用途是将基于秩的方法应用于连续响应，以便获得稳健的推断。例如，后面描述的比例优势模型允许连续Y，实际上是Wilcoxon-Mann-Whitney秩检验的推广。因此，半参数比例优势模型是普通线性模型的直接竞争者。
Many medical and epidemiologic studies incorporate an ordinal response variable. In some cases an ordinal responseYrepresents levels of a standard measurement scale such as severity of pain (none, mild, moderate, severe). In other cases, ordinal responses are constructed by specifying a hierarchy of separate endpoints. For example, clinicians may specify an ordering of the severity of several component events and assign patients to the worst event present from among none, heart attack, disabling stroke, and death. Still another use of ordinal response methods is the application of rank-based methods to continuous responses so as to obtain robust inferences. For example, the proportional odds model described later allows for a continuousYand is really a generalization of the Wilcoxon–Mann–Whitney rank test. Thus the semiparametric proportional odds model is a direct competitor of ordinary linear models.