A classification tree approach to the development of actuarial violence risk assessment tools.

TLDR

Since the 1970s, research has shown that actuarial tools can improve violence risk assessment accuracy, yet they remain largely research instruments and are based on main‑effects regression models that fail to capture the contingent nature of clinical assessments. The authors propose using a classification tree instead of a main‑effects regression approach to enhance the clinical utility of actuarial violence risk assessment tools. They also introduce two decision thresholds for high‑ and low‑risk classification, moving beyond the standard single threshold to improve dichotomous risk decisions. Empirical data from the MacArthur Violence Risk Assessment Study support these claims.

Abstract

Since the 1970s, a wide body of research has suggested that the accuracy of clinical risk assessments of violence might be increased if clinicians used actuarial tools. Despite considerable progress in recent years in the development of such tools for violence risk assessment, they remain primarily research instruments, largely ignored in daily clinical practice. We argue that because most existing actuarial tools are based on a main effects regression approach, they do not adequately reflect the contingent nature of the clinical assessment processes. To enhance the use of actuarial violence risk assessment tools, we propose a classification tree rather than a main effects regression approach. In addition, we suggest that by employing two decision thresholds for identifying high- and low-risk cases--instead of the standard single threshold--the use of actuarial tools to make dichotomous risk classification decisions may be further enhanced. These claims are supported with empirical data from the MacArthur Violence Risk Assessment Study.