Optimizing substance misuse prevention and treatment interventions for enhanced public health impact: Incorporating Bayesian decision analytics into the multiphase optimization strategy

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PROJECT SUMMARY Behavioral and biobehavioral interventions play a critically important role in the prevention and treatment of substance misuse (SM) and HIV. Developing interventions that have maximal public health impact is a priority for NIDA. To have maximal public health impact, interventions must be not only effective, but also affordable, readily implementable, and scalable—i.e., capable of having wide reach. The multiphase optimization strategy (MOST) is an innovative, engineering-inspired framework for developing, optimizing, and evaluating behavioral and biobehavioral interventions that have high public health impact. In MOST, an optimization phase of research precedes evaluation by randomized control trial. In the optimization phase, a randomized, powered optimization trial estimates the individual and combined effects of intervention components. Then, based on the results of the optimization trial, investigators decide which components to include in the optimized intervention; the objective of decision-making is to identify the set of intervention components that yields the best expected outcome while remaining affordable. The current methods of decision-making in the optimization phase of MOST are based on classical hypothesis testing, a frequentist approach. However, Bayesian methods are better equipped to answer the questions that motivate decision-making, questions like “What is the probability that a particular set of intervention components yields the best outcome (e.g. the biggest reduction in SM)?” We hypothesize that a Bayesian decision analytic approach to decision-making will more successfully identify optimal interventions—and that more successful decision-making will yield prevention and treatment interventions that have greater public health impact. With the support of a team of expert, renowned mentors (Dr. Linda M. Collins and Dr. David Vanness), the applicant will incorporate Bayesian methods into the MOST framework by evaluating a novel strategy for optimization using decision analytics (SODA). The applicant will develop software for SODA, evaluate SODA's performance in Monte Carlo simulation (Aim 1), and then use SODA to make decisions in a NIDA-funded optimization trial in the SM and HIV area, Heart to Heart 2 (HTH2; R01 DA040480; PIs: Gwadz and Collins), which targets both behavioral outcomes (e.g. SM) and biological outcomes (e.g. HIV viral load). Eventually, intervention scientists will be able to use SODA in their own applications of MOST, e.g. to optimize their SM interventions for greater public health impact. This F31 fellowship will give the applicant cutting-edge training in innovative methodologies from Bayesian decision analysis, health economics, and decision sciences; in methods dissemination and, specifically, the development of data visualization tools; in SM prevention and treatment; and in scientific writing, grant-writing, and the responsible conduct of research. The F31 will also give the applicant crucial protected time to advance toward her goal of a productive career as an independent research scientist working in the development of methods for optimization of interventions for the prevention and treatment of SM and HIV.