Modeling categorical variables by logistic regression.
OBJECTIVE
To demonstrate the use of logistic regression in health care research.


METHODS
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.
Peng
C Y
CY
Department of Counseling and Educational Psychology, Indiana University-Bloomington, 47405-1006, USA. peng@indiana.edu
Manz
B D
BD
Keck
J
J
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D003863:Depression / Q000209:etiology; D005221:Fatigue / Q000209:etiology; D005260:Female; D015438:Health Behavior; D006302:Health Services Research / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015146:Midwestern United States; D008960:Models, Psychological; D009369:Neoplasms / Q000150:complications* / Q000188:drug therapy; D010146:Pain / Q000145:classification / Q000209:etiology*; D007319:Sleep Initiation and Maintenance Disorders / Q000209:etiology; D014481:United States