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Modeling Categorical Variables by Logistic Regression

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Objective: To demonstrate the use of logistic regression in health care research. Method: Forward and backward stepwise logistic regression algorithms were systematically applied to a real-world data set comprising 301 cancer patients and a set of explanatory variables. Results: Four variables were identified as effective predictors of pain reporting by cancer patients during chemotherapy: fatigue, depression, severity of colds or viral infections, and insomnia. The 4-predictor model was validated by (a) significance tests of regression coefficients at p<0.05, (b) significant improvement of this model over competing models, and (c) goodness of fit indices. Conclusions: Logistic regression is useful for health-related research in which outcomes of interest are often categorical.
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Document Type: Research Article

Affiliations: 1: Department of Counseling and Educational Psychology, Indiana University-Bloomington, Bloomington, IN. 2: School of Nursing, University of Nebraska Medical Center, Omaha, NE. 3: School of Nursing, Indiana University-Purdue University at Indianapolis, Indianapolis, IN.

Publication date: 01 May 2001

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  • The American Journal of Health Behavior seeks to improve the quality of life through multidisciplinary health efforts in fostering a better understanding of the multidimensional nature of both individuals and social systems as they relate to health behaviors.

    The Journal aims to provide a comprehensive understanding of the impact of personal attributes, personality characteristics, behavior patterns, social structure, and processes on health maintenance, health restoration, and health improvement; to disseminate knowledge of holistic, multidisciplinary approaches to designing and implementing effective health programs; and to showcase health behavior analysis skills that have been proven to affect health improvement and recovery.

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