Literature DB >> 23408459

Predictive validity performance indicators in violence risk assessment: a methodological primer.

Jay P Singh1.   

Abstract

The predictive validity of violence risk assessments can be divided into two components: calibration and discrimination. The most common performance indicator used to measure the predictive validity of structured risk assessments, the area under the receiver operating characteristic curve (AUC), measures the latter component but not the former. As it does not capture how well a risk assessment tool's predictions of risk agree with actual observed risk, the AUC provides an incomplete portrayal of predictive validity. This primer provides an overview of calibration and discrimination performance indicators that measure global performance, performance in identifying higher-risk groups, and performance in identifying lower-risk groups. It is recommended that future research into the predictive validity of violence risk assessment tools includes a number of performance indicators that measure different facets of predictive validity and that the limitations of reported indicators be routinely explicated.
Copyright © 2013 John Wiley & Sons, Ltd.

Mesh:

Year:  2013        PMID: 23408459     DOI: 10.1002/bsl.2052

Source DB:  PubMed          Journal:  Behav Sci Law        ISSN: 0735-3936


  8 in total

1.  Predicting Sexual Assault Perpetration in the U.S. Army Using Administrative Data.

Authors:  Anthony J Rosellini; John Monahan; Amy E Street; Maria V Petukhova; Nancy A Sampson; David M Benedek; Paul Bliese; Murray B Stein; Robert J Ursano; Ronald C Kessler
Journal:  Am J Prev Med       Date:  2017-08-14       Impact factor: 5.043

2.  The development and validation of the Youth Actuarial Care Needs Assessment Tool for Non-Offenders (Y-ACNAT-NO).

Authors:  Mark Assink; Claudia E van der Put; Frans J Oort; Geert Jan J M Stams
Journal:  BMC Psychiatry       Date:  2015-03-04       Impact factor: 3.630

Review 3.  Authorship bias in violence risk assessment? A systematic review and meta-analysis.

Authors:  Jay P Singh; Martin Grann; Seena Fazel
Journal:  PLoS One       Date:  2013-09-02       Impact factor: 3.240

4.  Replicating the violence risk appraisal guide: a total forensic cohort study.

Authors:  Astrid Rossegger; Jérôme Endrass; Juliane Gerth; Jay P Singh
Journal:  PLoS One       Date:  2014-03-14       Impact factor: 3.240

Review 5.  Violence risk assessment in psychiatric patients in China: A systematic review.

Authors:  Jiansong Zhou; Katrina Witt; Yutao Xiang; Xiaomin Zhu; Xiaoping Wang; Seena Fazel
Journal:  Aust N Z J Psychiatry       Date:  2015-05-19       Impact factor: 5.744

6.  Predicting Reoffending Using the Structured Assessment of Violence Risk in Youth (SAVRY): A 5-Year Follow-Up Study of Male Juvenile Offenders in Hunan Province, China.

Authors:  Jiansong Zhou; Katrina Witt; Xia Cao; Chen Chen; Xiaoping Wang
Journal:  PLoS One       Date:  2017-01-11       Impact factor: 3.240

7.  Use of risk assessment instruments to predict violence in forensic psychiatric hospitals: a systematic review and meta-analysis.

Authors:  Taanvi Ramesh; Artemis Igoumenou; Maria Vazquez Montes; Seena Fazel
Journal:  Eur Psychiatry       Date:  2018-04-04       Impact factor: 5.361

8.  Risk assessment tools in criminal justice and forensic psychiatry: The need for better data.

Authors:  T Douglas; J Pugh; I Singh; J Savulescu; S Fazel
Journal:  Eur Psychiatry       Date:  2016-12-28       Impact factor: 5.361

  8 in total

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