IngentaConnect A Bayesian model for estimating the effects of drug use when drug...

Authors: McMillan, Garnett P.; Bedrick, Edward¹; C'deBaca, Janet²

Source: Addiction, Volume 104, Number 11, November 2009 , pp. 1820-1826(7)

Abstract:

Aims

We present a statistical model for evaluating the effects of substance use when substance use might be under-reported. The model is a special case of the Bayesian formulation of the `classical' measurement error model, requiring that the analyst quantify prior beliefs about rates of under-reporting and the true prevalence of substance use in the study population. Design

Prospective study. Setting

A diversion program for youths on probation for drug-related crimes. Participants

A total of 257 youths at risk for re-incarceration. Measurements

The effects of true cocaine use on recidivism risks while accounting for possible under-reporting. Findings

The proposed model showed a 60% lower mean time to re-incarceration among actual cocaine users. This effect size is about 75% larger than that estimated in the analysis that relies only on self-reported cocaine use. Sensitivity analysis comparing different prior beliefs about prevalence of cocaine use and rates of under-reporting universally indicate larger effects than the analysis that assumes that everyone tells the truth about their drug use. Conclusion

The proposed Bayesian model allows one to estimate the effect of actual drug use on study outcome measures.

Keywords: Adolescent drug use; Bayesian methods; cocaine use; criminal justice; measurement error; recidivism; self-report; under-reporting

Document Type: Research article

DOI: 10.1111/j.1360-0443.2009.02644.x

Affiliations: 1: Department of Mathematics and Statistics at the University of New Mexico, Albuquerque, NM, USA 2: Behavioral Health Research Center of the Southwest, A Center of the Pacific Institute for Research and Evaluation, Albuquerque, NM, USA and

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A Bayesian model for estimating the effects of drug use when drug use may be under-reported