Tests for goodness of fit in ordinal logistic regression models

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.

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Discussion on “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test” by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow

Biometrics ◽

10.1111/biom.13251 ◽

2020 ◽

Vol 76 (2) ◽

pp. 564-568

Author(s):

Ivy Liu ◽

Daniel Fernández

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Goodness Of Fit ◽

Logistic Regression Models ◽

Large Samples ◽

Lemeshow Test

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On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

Methods of Information in Medicine ◽

10.3414/me0571 ◽

2009 ◽

Vol 48 (03) ◽

pp. 306-310 ◽

Cited By ~ 4

Author(s):

C. E. Minder ◽

G. Gillmann

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Regression Models ◽

Goodness Of Fit ◽

Graphical Method ◽

Logistic Models ◽

Binary Logistic Regression ◽

Logistic Regression Models ◽

Logistic Analysis ◽

Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

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