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The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies

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  • Fazel, Seena
  • Burghart, Matthias
  • Fanshawe, Thomas
  • Gil, Sharon Danielle
  • Monahan, John
  • Yu, Rongqin

Abstract

Although risk assessment tools have been widely used to inform sentencing decisions, there is uncertainty about the extent and quality of evidence of their predictive performance when validated in new samples. Following PRISMA guidelines, we conducted a systematic review of validation studies of 11 commonly used risk assessment tools for sentencing. We identified 36 studies with 597,665 participants, among which were 27 independent validation studies with 177,711 individuals. Overall, the predictive performance of the included risk assessment tools was mixed, and ranged from poor to moderate. Tool performance was typically overestimated in studies with smaller sample sizes or studies in which tool developers were co-authors. Most studies only reported area under the curve (AUC), which ranged from 0.57 to 0.75 in independent studies with more than 500 participants. The majority did not report key performance measures, such as calibration and rates of false positives and negatives. In addition, most validation studies had a high risk of bias, partly due to inappropriate analytical approach used. We conclude that the research priority is for future investigations to address the key methodological shortcomings identified in this review, and policy makers should enable this research. More sufficiently powered independent validation studies are necessary.

Suggested Citation

  • Fazel, Seena & Burghart, Matthias & Fanshawe, Thomas & Gil, Sharon Danielle & Monahan, John & Yu, Rongqin, 2022. "The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies," Journal of Criminal Justice, Elsevier, vol. 81(C).
  • Handle: RePEc:eee:jcjust:v:81:y:2022:i:c:s0047235222000228
    DOI: 10.1016/j.jcrimjus.2022.101902
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    References listed on IDEAS

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    1. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    2. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    3. Jay P Singh & Martin Grann & Seena Fazel, 2013. "Authorship Bias in Violence Risk Assessment? A Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.
    4. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
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