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Commentary on Friedmann et al. (2012): the importance of framing effects and popular understanding in drug policy research.

Framing matters. If, on the morning after the 2012 Super Bowl, newspaper headlines heralded ‘Patriots dominated the second quarter’, that would be true, and would reflect a laudable achievement by the Patriots. Nevertheless, it would be a misleading summary of the game (the Giants won). Addiction published results of the Step’n Out evaluation, a multi-site trial of collaborative behavioral management (CBM) [1]. The intervention aimed at reducing illicit drug use and crime among drug-involved parolees. The authors’ research design and the potential for an effective and practicable intervention are encouraging. Their paper, however, frames an incidental finding as the principal result, which obscures the more fundamental, null finding. Of the carefully studied behavioral interventions for drug-involved offenders, contingency management (CM) has been shown to produce substantial reductions in drug use [2,3]. However, CM is rarely used in community corrections because most professionals balk at the idea of paying offenders not to do something they are not supposed to do in the first place [4]. One of the great appeals of CBM is that it shares the behavioral principles underlying CM but is packaged in a more politically palatable program. With a clearly articulated behavioral contract, reinforced with a series of graduated rewards and sanctions that can be delivered at a relatively low cost, Friedmann et al. offer a test of a model that could have important policy implications. That is, if the model yields improvements in outcomes. Reviews by Weisburd and colleagues have shown that very few approaches to managing offenders can withstand the scrutiny of a rigorous research design [5]. Standing out in this field, Friedmann et al. have executed a multi-site randomized controlled trial using an intentto-treat (ITT) design, the gold standard in evaluation research. Any conclusions of this study that are based on the original design warrant attention, and would be a substantial contribution to our limited knowledge about what works for this population. The principal conclusion of this study should be that the ITT randomized controlled trial of CBM showed no differences in outcomes compared with parole-as-usual. However, as is common in such circumstances, the authors appear to have identified a subsidiary, statistically significant result. CBM shows promise for reducing self-reported drug use among parolees who are primary ‘marijuana and “other” users’ (or, as they are sometimes described, ‘non-hard drugs’ users). That the authors emphasize the results of a subset of the population (non-hard drug users) limits the value of this paper. Marijuana users are only 16% of the entire sample. Results are reported for the category ‘marijuana and other’, but policy conclusions that follow from these results are drawn with respect to marijuana alone (which the authors note accounts for the majority of drugviolation arrests in the United States). This conflation of marijuana with (a diverse group of) other drugs is unfortunate, as parolees reporting marijuana as their primary drug are a minority even within this grouping, so it is not evident whether the results for the whole obtain with marijuana users. It is also not clear why the authors reported effects only on self-reported use, when they had drug-test results as well. Given the modest level of agreement between self-reported drug use and the urinalysis results (a reported kappa of only 0.38), it would have been helpful to at least have a footnote clarifying if the effects were comparable. This apparent shift in emphasis from the primary nullfinding to a positive subfinding could divert attention from the former, as these results will make their way into meta-analyses and into practitioner journals and will probably present a rosier view of the impacts of the program than the data warrant. However, in a field where so little shows promise we absolutely should attend to the rare effort that does. Friedmann and his team executed a rigorous study. Given the face validity of CBM, we need to know why it did not perform better in this trial. Did the researchers lack statistical power to draw stronger conclusions because (as they describe) they fell short of their recruitment goal, or do the challenges lie in program design, or in implementation? It is worth testing if, perhaps, CBM could demonstrate improved outcomes with a different implementation and with a larger sample to support the analysis—or with a sample of marijuana users in a prospective study.