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A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes

A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes
A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes
Ordinal variables are very often objects of study in health sciences. However, due to the lack of dissemination of models suited for ordinal variables, users often adopt other practices that result in the loss of statistical power. In this tutorial, different models from the family of logistic regression models are introduced as alternatives to handle and interpret ordinal outcomes. The models that were considered include: ordinal regression model (ORM), continuation ratio model (CRM), adjacent category model (ACM), generalised ordered logit model, sequential model, multinomial logit model, partial proportional odds model, partial continuation ratio model and stereotype ordered regression model. By using the relationship of hospital length of stay in a public hospital in Mexico with patient characteristics as an example, the models were used to describe the nature of such relationship and to predict the length of stay category to which a patient is most likely to belong. After an initial analysis, the ORM, CRM and ACM proved to be unsuitable for our data due to the transgression of the parallel regression assumption. The rest of the models were estimated in STATA. The results suggested analogous directionality of the parameter estimates between models, although the interpretation of the odds ratios varied from one model to another. Performance measurements indicated that the models had similar prediction performance. Therefore, when there is an interest in exploiting the ordinal nature of an outcome, there is no reason to maintain practices that ignore such nature since the models discussed here proved to be computationally inexpensive and easy to estimate, analyse and interpret.
logistic regression, ordinal variables, generalised ordered logit, partial proportional odds, multinomial logit, stereotype ordered regression
1387-3741
223-240
Guzman-Castillo, Maria
53be3e74-a554-49b5-a744-ed447cc1025a
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Luke, Michelle
1251cb5d-cd8a-40c9-b8d0-974f993b1448
Smith, Honora
1eaef6a6-4b9c-4997-9163-137b956c06b5
Guzman-Castillo, Maria
53be3e74-a554-49b5-a744-ed447cc1025a
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Luke, Michelle
1251cb5d-cd8a-40c9-b8d0-974f993b1448
Smith, Honora
1eaef6a6-4b9c-4997-9163-137b956c06b5

Guzman-Castillo, Maria, Brailsford, Sally, Luke, Michelle and Smith, Honora (2015) A tutorial on selecting and interpreting predictive models for ordinal health-related outcomes. Health Services and Outcomes Research Methodology, 15 (3-4), 223-240. (doi:10.1007/s10742-015-0140-6).

Record type: Article

Abstract

Ordinal variables are very often objects of study in health sciences. However, due to the lack of dissemination of models suited for ordinal variables, users often adopt other practices that result in the loss of statistical power. In this tutorial, different models from the family of logistic regression models are introduced as alternatives to handle and interpret ordinal outcomes. The models that were considered include: ordinal regression model (ORM), continuation ratio model (CRM), adjacent category model (ACM), generalised ordered logit model, sequential model, multinomial logit model, partial proportional odds model, partial continuation ratio model and stereotype ordered regression model. By using the relationship of hospital length of stay in a public hospital in Mexico with patient characteristics as an example, the models were used to describe the nature of such relationship and to predict the length of stay category to which a patient is most likely to belong. After an initial analysis, the ORM, CRM and ACM proved to be unsuitable for our data due to the transgression of the parallel regression assumption. The rest of the models were estimated in STATA. The results suggested analogous directionality of the parameter estimates between models, although the interpretation of the odds ratios varied from one model to another. Performance measurements indicated that the models had similar prediction performance. Therefore, when there is an interest in exploiting the ordinal nature of an outcome, there is no reason to maintain practices that ignore such nature since the models discussed here proved to be computationally inexpensive and easy to estimate, analyse and interpret.

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Accepted/In Press date: 11 June 2015
e-pub ahead of print date: 1 July 2015
Published date: December 2015
Keywords: logistic regression, ordinal variables, generalised ordered logit, partial proportional odds, multinomial logit, stereotype ordered regression
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 381754
URI: http://eprints.soton.ac.uk/id/eprint/381754
ISSN: 1387-3741
PURE UUID: 63b195d9-5056-4d77-b42a-8d19f1bca509
ORCID for Sally Brailsford: ORCID iD orcid.org/0000-0002-6665-8230
ORCID for Honora Smith: ORCID iD orcid.org/0000-0002-4974-3011

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Date deposited: 22 Sep 2015 15:52
Last modified: 15 Mar 2024 03:23

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Contributors

Author: Maria Guzman-Castillo
Author: Michelle Luke
Author: Honora Smith ORCID iD

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