Predicting outcomes of inpatient psychotherapy using quality management data: comparing classification and regression trees with logistic regression and linear discriminant analysis.
Abstract   Three classification models are compared with regard to their ability to identify unsuccessful treatment outcomes. Data from the Stuttgart-Heidelberg model on quality management of 1,401 inpatients were used. According to the evaluation algorithm used, 82% benefited from therapy. Outcomes were classified using logistic regression, linear discriminant analysis, and classification and regression trees. For the regression techniques, variables were selected stepwise backward; for the CART model, the method-inherent selection scheme was adopted. Because there were few signal cases in the data, these cases were double weighted. Model performance was obtained using classic cross-validation. Overall misclassification rates did not differ between methods for the construction sample but differed when applied to the validation sample. Overall classification rates for the given problem are modest.
a      Institute for Medical Psychology   ,  Ernst-Moritz-Arndt-University and Center for Psychotherapy Research   ,  Stuttgart.
Wolfgang
W
Hannöver
Hans
H
Kordy