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A Protocol for Measuring Cue Reactivity in a Rat Model of Cocaine Use Disorder
Published on: June 18, 2018
A Bayesian model for estimating the effects of drug use when drug use may be under-reported
Garnett P McMillan1, Edward Bedrick, Janet C'deBaca
1Behavioral Health Research Center of the Southwest, A Center of the Pacific Institute for Research and Evaluation, Albuquerque, NM 87102, USA. gmcmillan@bhrcs.org
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.
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