title: Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.
abstract: BACKGROUND
  Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices.
  
  
  METHODS
  This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models.
  
  
  RESULTS
  CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model.
  
  
  CONCLUSIONS
  The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.
authors.affiliation: Department of Community Medicine, The Medical School, Shiraz University of Medical Sciences, Shiraz, Islamic Republic of Iran. shayanz@sums.ac.ir.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Shayan
authors.forename: Naser
authors.initials: N
authors.lastname: Mohammad Gholi Mezerji
authors.forename: Leila
authors.initials: L
authors.lastname: Shayan
authors.forename: Parisa
authors.initials: P
authors.lastname: Naseri
mesh_terms: D003430:Cross-Sectional Studies; D003710:Demography; D003863:Depression / Q000209:etiology*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D009369:Neoplasms / Q000523:psychology*; D011237:Predictive Value of Tests