Garnett P McMillan1, Edward Bedrick, Janet C'deBaca. 1. Behavioral Health Research Center of the Southwest, A Center of the Pacific Institute for Research and Evaluation, Albuquerque, NM 87102, USA. gmcmillan@bhrcs.org
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.
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.