摘要：Increasing integration and availability of data on large groups of persons has been accompanied by proliferation of statistical and other algorithmic prediction tools in banking, insurance, marketing, medicine, and other fields (see, e.g., Steyerberg 2009a 43. Steyerberg, E.W. (2009a), Clinical Prediction Models. A Practical Approach to Development, Validation, and Updating, New York: Springer-Verlag. View all references , b 44. Steyerberg, E.W. (2009b), “Prediction Rules and Modeling,” in Encyclopedia of Medical Decision Making (vol. 2), ed. M. W. Kattan, Thousand Oaks, CA: Sage, pp. 893–897. [CrossRef] View all references ). Controversy may ensue when such tools are introduced to fields traditionally reliant on individual clinical evaluations. Such controversy has arisen about “actuarial” assessments of violence recidivism risk, that is, the probability that someone found to have committed a violent act will commit another during a specified period. Recently, Hart, Michie, and Cooke ( 2007a 24. Hart, S.D., Michie, C., Cooke, D.J. (2007a), Precision of Actuarial Risk Assessment Instruments. Evaluating the ‘Margins of Error’ of Group v. Individual Predictions of Violence, British Journal of Psychiatry, 190, s60–s65. [CrossRef] , [Web of Science ®] View all references ) and subsequent papers from these authors in several reputable journals have claimed to demonstrate that statistical assessments of such risks are inherently too imprecise to be useful, using arguments that would seem to apply to statistical risk prediction quite broadly. This commentary examines these arguments from a technical statistical perspective, and finds them seriously mistaken in many particulars. They should play no role in reasoned discussions of violence recidivism risk assessment.
