Optimization of Behavioral, Biobehavioral, and Biomedical Interventions

Contributed chapters cover statistical analysis, adaptive interventions and real-world implementation examples
Makes formal optimization of behavioral and biobehavioral interventions easy to implement in research and practice
Builds on methodology presented in monograph by Linda Collins, "Optimization of Behavioral and Biobehavioral Interventions" (Springer, 2018)

Auteur
Linda M. Collins is Distinguished Professor of Human Development and Family Studies, Professor of Statistics, and the director of The Methodology Center at the Pennsylvania State University. She is interested in most aspects of research methods. Her current work focuses on experimental and non-experimental design, particularly for building, optimizing and evaluating behavioral, biobehavioral, and biomedical interventions. Professor Collins also has a long-standing interest in models for longitudinal data, particularly latent transition analysis, and other latent class models.
Kari C. Kugler is an Assistant Teaching Professor in Biobehavioral Health and an Affiliate of the Methodology Center at Penn State. Trained as a behavioral epidemiologist, her work focuses on the design and analysis of multi-component, multi-level interventions targeting a wide range of health behaviors among various populations and contexts. She collaborates with intervention scientists on building highly effective and efficient behavioral interventions.

Contenu

1. Using the Multiphase Optimization Strategy (MOST) to Develop an Optimized Online STI Preventive Intervention Aimed at College Students: Description of Conceptual Model and Iterative Approach to Optimization.- 2. implementing factorial experiments in real-world settings: Lessons learned while engineering an optimized smoking cessation treatment.- 3. Multilevel Factorial Designs in Intervention Development.- 4. Experimental Designs for Research on Adaptive Interventions: Singly and Sequentially Randomized Trials.- 5. Intensively Adaptive Interventions Using Control Systems Engineering: Two Illustrative Examples.- 6. Coding and interpretation of effects in analysis of data from a factorial experiment.- 7. Optimizing the Cost-Effectiveness of a Multicomponent Intervention Using Data from a Factorial Experiment: Considerations, Open Questions, and Tradeoffs Among Multiple Outcome.- 8. Investigating an Intervention's Causal Story: Mediation Analysis Using a Factorial Experiment andMultiple Mediators.