Modeling categorical variables by logistic regression

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.