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.
