Competing Risks, Persistence, and Desistance in Analyzing Recidivism

A statistical procedure is developed to analyze recidivism in samples whichare subject to the presence of desisters and to multiple modes ofreconviction. This allows for a more accurate study of individuals'transition and hazard in the type and timing of offenses following aspecific type of con...

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Bibliographic Details
Main Author: Escarela, Gabriel (Author)
Contributors: Francis, Brian ; Soothill, Keith
Format: Electronic Article
Language:English
Published: 2000
In: Journal of quantitative criminology
Year: 2000, Volume: 16, Issue: 4, Pages: 385-414
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Keywords:
Indecent assault
SEX offenders
Survival Analysis
event-specific hazard
split population models
Description
Summary:A statistical procedure is developed to analyze recidivism in samples whichare subject to the presence of desisters and to multiple modes ofreconviction. This allows for a more accurate study of individuals'transition and hazard in the type and timing of offenses following aspecific type of conviction. The use of a nonparametric approach forinvestigating failure in the presence of other acting causes is shown;initial estimators of the probabilities of reconviction for different typesof offenses are obtained, and the method can be used both to display thedata and to choose an appropriate parametric family for the survivaltimes. An exponential mixture model for competing risks is presented insuch a way that it allows us to adjust for concomitant variables and toassess their effects on the probabilities both of reconviction forpredetermined types of offenses and desistance and of the hazards ofreconviction; a method for assessing calibration of predicted survivalprobabilities is suggested. A 21-year follow-up of persons convicted ofindecent assault on a female in 1973 illustrates the methods; we find ahigh probability of sexual reconviction for individuals with previoussexual convictions and evidence of diversity and a raised hazard ofreconviction for young chronic offenders.
ISSN:1573-7799
DOI:10.1023/A:1007586031274