A comparison of statistical models in predicting violence in psychotic illness

Stuart Thomas, Morven Leese, Elizabeth Walsh, Paul McCrone, Paul Moran, Tom Burns, Francis Creed, Peter Tyrer, Thomas Fahy

    Research output: Contribution to journalArticlepeer-review


    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. © 2005 Elsevier Inc. All rights reserved.
    Original languageEnglish
    Pages (from-to)296-303
    Number of pages7
    JournalComprehensive Psychiatry
    Issue number4
    Publication statusPublished - Jul 2005


    • Adolescent
    • Adult
    • Aged
    • Comorbidity
    • Female
    • Humans
    • Logistic Models
    • Male
    • Middle Aged
    • epidemiology: Personality Disorders
    • epidemiology: Psychotic Disorders
    • Risk Factors
    • statistics & numerical data: Violence


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