Abreu Mery Natali Silva, Siqueira Arminda Lucia, Cardoso Clareci Silva, Caiaffa Waleska Teixeira
Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.
Cad Saude Publica. 2008;24 Suppl 4:s581-91. doi: 10.1590/s0102-311x2008001600010.
Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.
近年来，生活质量在公共卫生研究中越来越受到重视。通常，生活质量的结果是通过有序量表来衡量的。在这些情况下，需要特定的统计方法，因为诸如二分法或结果变量分布的错误信息等程序可能会使推理过程变得复杂。有序逻辑回归模型在许多此类情况下是合适的。本文对比例优势模型、部分比例优势模型、连续比例模型和刻板印象模型进行了综述。通过一项对273例精神分裂症患者生活质量的研究数据，说明了模型的拟合、统计推断和模型间的比较。所有测试模型均显示出良好的拟合，但由于数据的性质和结果易于解释，比例优势或部分比例优势模型被证明是最佳选择。有序逻辑模型根据结果的分类、与假设的充分性、拟合优度和简约性表现出不同的性能。
