Randomized control trials have been the standard method by which to judge the efficacy of drug prevention programs. However, once adopted by schools and agencies for delivery, randomized control trials can rarely be conducted on disseminated programs. In considering this challenge, I developed an alternative method—virtual controls—that use what is known from previously completed research projects to estimate the prevalence of drug use that would be expected of an untreated control group. Many intervention and epidemiology research projects have been completed during the past 40 years. I sought data from intervention projects that included non-treated control groups and from longitudinal epidemiology projects. I harmonized the data so that psychosocial measures would all be comparable. I then integrated these diverse datasets into one larger dataset. From this harmonized and integrated dataset, I identified psychosocial measures that were strong predictors of past 30-day alcohol, cigarette, and marijuana use. I created a composite psychosocial score for the integrated dataset. To create virtual control group cases, I calculated percentile scores for psychosocial scores separately for each gender and age group. Percentile scores thus served as an individual virtual control case. The virtual control method matches each treated student’s pretest psychosocial score to a virtual case. The virtual case “matures” as was projected based on the integrated dataset. Using logistic regression formulas, posttest probability of use among virtual cases is then computed and compared with observed treatment groups’ prevalence of use.