Optimization of Behavioral and Biobehavioral Interventions

Catalog description

Evidence-based behavioral and biobehavioral interventions are used to prevent and treat health problems (e.g. school-based drug abuse prevention; smoking cessation treatment), improve educational attainment (e.g. reading improvement interventions), and promote health and well-being (e.g. parenting skills training). An intervention may be aimed at any age and delivered in any context; may be aimed at individuals, families, organizations, or communities; and may include both behavioral components and medical components such as pharmaceuticals. The purpose of this course is to enable students to understand and apply quantitative, empirical research methods for optimization of evidence-based multicomponent behavioral and biobehavioral interventions. These methods are used for two related purposes. First, they are used to obtain knowledge about what intervention components work and for whom. Second, they are used to build optimized evidence-based interventions that are not only effective, but also efficient, economical, and scalable. The methods can be used to build new interventions, improve existing interventions, or identify good approaches for implementing interventions. The course will cover a comprehensive framework for empirical development, optimization, and evaluation of evidence-based behavioral and biobehavioral interventions. Students will learn how to craft a detailed conceptual model for an intervention under development, based on existing scientific theory and literature, and the student's own ideas. A substantial amount of time in the course will be spent on experimental design for optimization trials, particularly factorial experimental designs and variations such as the fractional factorial. The emphasis will be on making the best use of available resources so as to gather the highest-quality and most relevant scientific information. Students will learn how to identify the most appropriate and efficient experimental design for an optimization trial. Practical matters, such as guarding against implementation errors when conducting an experiment in a field setting, and dealing with errors if they occur, will be reviewed. Appropriate statistical analysis of data gathered during an optimization trial will be discussed. Students will learn how use the empirical results obtained in an optimization trial as a basis for selection of the components and component levels that will make up the optimized intervention. Students will also learn how the approaches covered in this course are applicable across a broad range of intervention types and objectives, and also to determine how these approaches are applicable to optimization of interventions in the students' own individual fields of scientific endeavor.