Work

A Note on Ordinal Modeling of Smoking Rate Data

Year: 2025

Type: article

Abstract: INTRODUCTION: This paper discusses statistical models for ordinal data that may be more appropriate for smoking rate outcomes than are models that assume continuous measurement and normality. Smoking rate outcomes often have distributions that make them inappropriate for many popular statistical models that assume normality, and are more appropriately considered ordinal outcomes. This article describes how the ordinal logistic regression model can be used to obtain estimates of means, and comparisons of means, for smoking rate outcomes. AIMS AND METHODS: Analyses of the daily smoking rate of a sample of 383 subjects are presented using linear regression and ordinal logistic regression. From the latter, we derive regression estimates (intercepts and slopes) in terms of the mean response without having to assume any distributional form for the smoking rate outcome variable. Regressors considered are the subject's gender and their level of dependency as assessed by the nicotine dependence symptom scale. RESULTS: Estimated regression coefficients were similar, but the linear regression model indicated a significant gender effect, such that females had a higher smoking rate than males. Though similar, this effect was not quite significant (at the 0.05 level) in the ordinal model. The effect of dependency was significant in both models, with more dependent smokers having a higher smoking rate. CONCLUSIONS: Results and conclusions can depend on the assumptions of a statistical model. Methods relaxing the assumption of normality are useful to examine how robust effects are to this common assumption.

Source: Nicotine & Tobacco Research

DOI: 10.1093/ntr/ntaf087

Authors: Donald Hedeker, Robin J Mermelstein, Juned Siddique

Institutions: University of Chicago, University of Illinois Chicago, Northwestern University

Language: English

Topic: Smoking Behavior and Cessation

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases