This course provides a foundation in the main study design approaches used to evaluate interventions in real-world implementation settings. Learners examine both non-randomized quasi-experimental designs (including Pre-Post and Interrupted Time Series designs), randomized designs (including pragmatic trials, cluster-randomized trials, stepped-wedge trials, factorial designs, MOST, SMART, and Choice/Preference designs), and hybrid effectiveness-implementation designs (Types I, II, III).
For each design, scholars assess core features, common pitfalls, and strategies to strengthen internal and external validity. Throughout the course, learners are challenged to apply each design to their 'real-world' implementation research questions and settings and to select design features to maximize overall study quality.
The course also introduces analytic approaches for each study design to allow learners to engage effectively with biostatisticians to advance study ideas from concept to implementation. The course will also invite interested learners to conduct more in-depth sample size calculations and analysis of real-world data in an optional Analysis Lab.
Course structure
IMS 241 is offered as a 2-unit Design course, with an optional 1-unit Analysis Lab.
The Design course constitutes the core curriculum and is required for all learners. Each week, learners will be asked to watch online lectures, review case studies, apply study designs to their own research questions and settings, and provide supportive written feedback on peer assignments.
At the start of the term, learners may elect to enroll in the optional Analysis Lab, which provides hands-on experience applying analytic methods to the study designs covered in the course. Each week, learners enrolled in the Analysis Lab compute sample size estimates and analyze real or simulated data sets, generating and interpreting results aligned with the weekly design topics.
Note for UCSF Graduate Division Students: The core Design course is worth 2 units, and the optional Analysis Lab is worth 1 additional unit. The Analysis Lab may only be taken in conjunction with the core course. Students are responsible for managing their enrollment (e.g., 2 vs. 3 units) with the registrar's office.
Learning Objectives
By the end of the course, scholars will be able to:
- Describe the key characteristics of, and rationale for choosing among, common non-randomized quasi-experimental study designs (e.g., pre-post, interrupted time series) and randomized study designs (e.g., pragmatic, cluster, stepped wedge, factorial, MOST, SMART, and choice/preference) used in real-world implementation research.
- Identify key threats to internal validity across study designs, including confounding, mediation, effect modification, selection bias, information and reporting bias, and random error, and propose design or analytic strategies to mitigate each threat.
- Design hybrid implementation-effectiveness studies and define appropriate implementation and effectiveness outcomes for each study design.
- Create clear visual diagrams for each study design for inclusion in protocols, presentations, and grant proposals.
- Develop a plan for discussing sampling strategy, sample size determination, and analytic approach with a biostatistician for each study design.
Optional Analysis Lab (1 unit)
In addition to the objectives above, leaners who enroll in the Analysis lab will be able to:
- Identify appropriate analytic methods for each study design covered in the course, including regression, segmented regression, autoregressive integrated moving-average (ARIMA) models, generalized estimating equations, and mixed-effects models.
- Calculate and interpret sample size estimates for each study design using STATA software.
- Conduct and interpret analyses in STATA using example datasets and code provided for each study design.
Audience
Clinicians, public health practitioners, and researchers seeking to build skills in selecting, designing, and analyzing studies that translate evidence into practice.
Offered: Spring Term
Faculty
Course Directors
Starley B. Shade, PhD, MPH, is a Professor and Head of the Division of Infectious Diseases and Global Epidemiology in the Department of Epidemiology & Biostatistics at UCSF. Dr. Shade’s research focuses on quantitative and economic evaluation of community- and clinic-level interventions to improve health outcomes for those with HIV, TB and Malaria, including cluster-randomized trials and adaptive study designs in Kenya and Uganda, as well as the 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 & Biostatistics, and the Department of Obstetrics, Gynecology, and Reproductive Sciences at UCSF. Dr. Brown has over 20 years of experience conducting health research in sub-Saharan Africa, with expertise in reproductive health, STI and HIV prevention, clinical trials, implementation science, and safer conception.