Regression models for patient-reported measures having ordered categories recorded on multiple occasions.
Regression models for patient-reported measures having ordered categories recorded on multiple occasions.
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DOI：
10.1111/j.1600-0528.2010.00583.x
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发表时间：
2011-04
影响因子：
2.3
通讯作者：
Schwartz TA
中科院分区：
文献类型：
--
作者：
Preisser JS;Phillips C;Perin J;Schwartz TA
本文回顾了牙科临床研究中经常使用的有序分类结果的比例和部分比例优势回归，如患者报告的测量方法。有序数据的比例优势回归模型是普通Logistic回归模型在二分回答问题上的推广。当比例赔率假设对一些但不是所有协变量成立时，鲜为人知的部分比例赔率模型提供了一个有用的扩展。序数数据模型用于分析重复的序数结果，以确定双侧矢状劈开截骨手术后与感觉改变相关的负担对于那些仅在手术后接受开放练习的患者和那些在接受感觉再训练练习的同时接受标准开放练习的患者是否不同。比例和偏比例优势模型广泛适用于牙科研究中的横断面和纵向序数数据的分析。
The article reviews proportional and partial proportional odds regression for ordered categorical outcomes, such as patient-reported measures, that are frequently used in clinical research in dentistry. The proportional odds regression model for ordinal data is a generalization of ordinary logistic regression for dichotomous responses. When the proportional odds assumption holds for some but not all of the covariates, the lesser known partial proportional odds model is shown to provide a useful extension. The ordinal data models are illustrated for the analysis of repeated ordinal outcomes to determine whether the burden associated with sensory alteration following a bilateral sagittal split osteotomy procedure differed for those patients who were given opening exercises only following surgery and those who received sensory retraining exercises in conjunction with standard opening exercises. Proportional and partial proportional odds models are broadly applicable to the analysis of cross-sectional and longitudinal ordinal data in dental research.
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