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)
Audience
Clinicians, public health practitioners, and researchers wishing to gain knowledge and skills in translating evidence into practice.
Offered: Spring Term
Faculty
Course Directors
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.
Joelle Brown, MPH, PhD, is an epidemiologist and Associate Professor in the Department of Epidemiology and Biostatistics and the Department of Obstetrics, Gynecology, and Reproductive Sciences at the University of California, San Francisco. She has over 20 years of experience conducting health research in sub-Saharan Africa. Her research and expertise include reproductive health and the prevention of sexually transmitted infections, including HIV, clinical trials, implementation science, and safer conception strategies for women and couples living with HIV.