Related Experiment Video
Updated: Aug 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Modeling categorical variables by logistic regression
1Department of Counseling and Educational Psychology, Indiana University-Bloomington, 47405-1006, USA. peng@indiana.edu
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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