Study Designs for Intervention Research in Real-World Settings

This course provides a foundation of the main components of alternatives to individual randomized control trials that can be used to evaluate interventions placed in real world settings. An overview of the history of experimental and observational design will set the stage to understand design variants. For each randomized (cluster-randomized and stepped-wedge randomized trials) and quasi-experimental design (QED) presented students will assess: what are the key features, common pitfalls, and possible strategies to improve internal and external validity. QEDs covered in this class include: pre-post designs and interrupted time series designs. Scholars will be also introduced to implications of design decisions for analytic approaches to data analysis, and how to prepare to meet with a biostatistician to take design ideas to the next level. Focus will be placed on determining which design is most suited to a range of 'real world' implementation settings and circumstances, and on choosing between designs to maximize overall study quality.

At the end of the course, scholars will be able to:

  • Describe the rationale for using common randomized and non-randomized (i.e., quasi-experimental) study design alternatives to individual participant randomized trials
  • Understand key characteristics of cluster-randomized trials, stepped-wedge randomized trials, and selected quasi-experimental designs (QEDs)
  • Identify specific threats to internal validity and strategies to mitigate these threats for cluster randomized trials, stepped wedge randomized trials, and selected quasi-experimental designs (QEDs)
  • Create diagrams of cluster-randomized trials, stepped-wedge randomized trials, and selected quasi-experimental designs (QEDs) for use in protocols and grant proposals
  • Develop design elements that could be incorporated to characterize and understand the implementation process, including hybrid effectiveness implementation designs
  • Prepare a checklist or plan for discussing issues of sampling, sample size, and analysis with a biostatistician for cluster-randomized trials, stepped-wedge randomized trials, and selected quasi-experimental designs (QEDs)


Clinicians, public health practitioners, and researchers wishing to gain knowledge and skills in translating evidence into practice.

Offered: Spring Term


Course Directors

Margaret Handley, PhD, MPH, is a public health trained epidemiologist and Professor in the Departments of Epidemiology and Biostatistics and Department of Medicine Center for Vulnerable Populations, at the University of California San Francisco. Her research focuses on implementation science and bridging the fields of primary care, public health, and health communication for underserved populations in US and global settings.

Dr. Handley also co-directs the UCSF Training Program in Implementation Science, in which she directs a course on developing theory-informed interventions. She has methodological expertise in practice-based research, community-engaged research, quasi-experimental designs, implementation science, and mixed methods. Her active grants include developing and pilot testing a post-partum intervention to reduce chronic disease risk in women with prior gestational diabetes and on improving health literacy skills among migrant adolescents.

Starley B. Shade, PhD, MPH, is an Associate Professor in Epidemiology and Biostatistics at UCSF. Her research focuses on quantitative and economic evaluation of community- and clinic-level interventions to improve health outcomes for those with HIV, TB and Malaria. Her current projects include cluster-randomized trials and adaptive study designs in Kenya and Uganda, as well as evaluation of demonstration interventions to improve engagement in HIV care among people seen in publicly-funded programs in the U.S.