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Relative Risk01:12

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Improving Risk Communication: Developing Risk Ratios for the VRAG-R.

Simon T Davies1, L Maaike Helmus2, Vernon L Quinsey3

  • 1Victoria University of Wellington, New Zealand.

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|April 23, 2020
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Summary
This summary is machine-generated.

Researchers created new risk ratios for the Violence Risk Appraisal Guide-Revised (VRAG-R) to improve risk communication. Logistic regression accurately estimated violent recidivism rates over 15 years.

Keywords:
community violencemental health and violenceviolent offenders

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Area of Science:

  • Forensic Psychology
  • Criminology
  • Risk Assessment

Background:

  • The Violence Risk Appraisal Guide-Revised (VRAG-R) is a tool used for assessing violence risk.
  • Existing risk communication methods may have limitations.
  • There is a need to align VRAG-R scores with standardized risk frameworks.

Purpose of the Study:

  • To develop risk ratios for the VRAG-R.
  • To enhance risk communication options for VRAG-R users.
  • To provide data for applying a standardized five-level risk framework to the VRAG-R.

Main Methods:

  • Analysis of a VRAG-R normative dataset (N = 1,238).
  • Comparison of logistic regression and Cox regression for estimating violent recidivism.
  • Calculation of risk ratios using rate ratios from a logistic regression model with a 5-year follow-up.

Main Results:

  • Logistic regression provided more accurate estimates of violent recidivism than Cox regression.
  • The relationship between VRAG-R scores and violent recidivism remained consistent over a 15-year follow-up.
  • Final risk ratios were computed using rate ratios from a logistic regression model.

Conclusions:

  • The developed risk ratios offer broader risk communication options for the VRAG-R.
  • Logistic regression is a suitable method for estimating recidivism rates with VRAG-R data.
  • The findings support the use of VRAG-R in conjunction with standardized risk frameworks.