title: Quantifying the contributions of three types of information to the prediction of criminal conviction using the receiver operating characteristic.
abstract: BACKGROUND
  Quantifying the contributions that different types of information make to the accurate prediction of offending offers the prospects of improved practice and better use of resources.
  
  
  OBJECTIVE
  To quantify the contributions made by three types of information--demographic data alone, demographic and criminal record and demographic, criminal record and legal class of disorder--to the prediction of criminal conviction in patients.
  
  
  METHODS
  All 425 patients discharged from the three special (high secure) hospitals in England and Wales over 2 years were followed for 10.5 years. The contribution of each type of information was described in terms of the area under the receiver operating characteristic curve (AUC) and the number needed to detain (NND).
  
  
  RESULTS
  The AUC values using the three types of information were 0.66, 0.72 and 0.73 respectively. Prediction based on the full model using an optimal probability cut-off implies an NND of 2. The AUCs for serious offences were 0.67, 0.69 and 0.75 respectively.
  
  
  CONCLUSIONS
  For long-term prediction of conviction on any charge, information on legal class adds little to the accuracy of predictions made using only a patient's age, gender and criminal record. In the prediction of serious offences alone the contribution of legal class is significant.
authors.affiliation: Yale University Department of Psychiatry, 34 Park Street, New Haven, CT 06519, USA. alec.buchanan@yale.edu
authors.forename: Alec
authors.initials: A
authors.lastname: Buchanan
authors.forename: Morven
authors.initials: M
authors.lastname: Leese
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D003415:Crime / Q000706:statistics & numerical data*; D003417:Criminal Psychology / Q000379:methods / Q000706:statistics & numerical data; D004739:England; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D014852:Wales