A comparison of statistical models in predicting violence in psychotic illness.
Thomas S., Leese M., Walsh E., McCrone P., Moran P., Burns T., Creed F., Tyrer P., Fahy T.
BACKGROUND: The application of statistical modeling techniques, including classification and regression trees, in the prediction of violence has increasingly received attention. METHODS: The predictive performance of logistic regression and classification tree methods in predicting violence was explored in a sample of patients with psychotic illness. RESULTS: Of 2 logistic regression models, the forward stepwise method produced a simpler model than the full model, but the latter performed better. The performance of the classification tree appeared to be high before cross-validation, but reduced when cross-validated. The standard logistic model was the most robust model. A simplified tree with extra weight given to violent cases was a reasonable competitor and was simple to apply. CONCLUSION: Although classification trees can be suitable for routine clinical practice, because of the simplicity of their decision-making processes, their robustness and therefore clinical utility was problematic in this sample. Further research is required to compare such models in large prospective epidemiologic studies of other psychiatric populations.