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

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.