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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. |