Ordinal logistic regression models: application in quality of life studies.
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.
Abreu
Mery Natali Silva
MN
Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brasil. merynatali@yahoo.com.br
Siqueira
Arminda Lucia
AL
Cardoso
Clareci Silva
CS
Caiaffa
Waleska Teixeira
WT
D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D016017:Odds Ratio; D011788:Quality of Life; D012044:Regression Analysis; D018401:Sample Size; D012565:Schizophrenic Psychology