title: Modeling categorical variables by logistic regression.
abstract: 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.
authors.affiliation: Department of Counseling and Educational Psychology, Indiana University-Bloomington, 47405-1006, USA. peng@indiana.edu
authors.forename: C Y
authors.initials: CY
authors.lastname: Peng
authors.forename: B D
authors.initials: BD
authors.lastname: Manz
authors.forename: J
authors.initials: J
authors.lastname: Keck
mesh_terms: 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