The Authors: Dr. Jessica Kasza, BSc, PhD, a research fellow in biostatistics at the Department of Epidemiology and Preventive Medicine at Monash University, has research interests that include healthcare provider comparison and the estimation of causal effects. Professor Rory Wolfe, BSc, PhD, Professor of Biostatistics at the School of Public Health and Preventive Medicine, has broad research interests in biostatistics.
INVITED REVIEW SERIES: MODERN STATISTICAL METHODS IN RESPIRATORY MEDICINE
Interpretation of commonly used statistical regression models
Article first published online: 23 DEC 2013
© 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology
Volume 19, Issue 1, pages 14–21, January 2014
How to Cite
Kasza, J. and Wolfe, R. (2014), Interpretation of commonly used statistical regression models. Respirology, 19: 14–21. doi: 10.1111/resp.12221
Series Editors: Rory Wolfe and Michael Abramson
- Issue published online: 23 DEC 2013
- Article first published online: 23 DEC 2013
- Manuscript Accepted: 1 OCT 2013
- Manuscript Received: 16 SEP 2013
- NHMRC Centre of Research Excellence grant. Grant Number: 1035261
- linear model;
- logistic model;
- ordinal logistic model;
- regression analysis.
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study.