EPI-BIO Lecture Series: Dr. Fan Li "Designing three-level cluster randomized trials to assess treatment effect heterogeneity"
The University of Kentucky's College of Public Health welcomes Dr. Fan Li, assistant professor in the Department of Biostatistics at Yale University School of Public Health, to the EPI/BIO Lecture Series on October 6th, 2022 as 12pm via Zoom. All CPH students, staff, and faculty are invited.
Designing three-level cluster randomized trials to assess treatment effect heterogeneity
Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among pre-specified patient subpopulations, such as those defined by demographics or baseline clinical characteristics.
In this presentation, we introduce novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level and participant-level effect modifiers and to designs where randomization can be carried out at any level.
We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and offer new insights from a study design perspective for conducting confirmatory analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model.
A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations. Other recent developments on methods for designing cluster randomized trials to detect treatment effect heterogeneity will also be discussed in the presentation.
Dr. Fan Li is an assistant professor in the Department of Biostatistics at Yale University School of Public Health. He is also faculty member at the Center for Methods in Implementation and Prevention Science (CMIPS) and the Yale Center for Analytical Sciences (YCAS).
Dr. Li receives his PhD in Biostatistics from Duke University in 2019. His research interests include developing methods for comparative effectiveness research with randomized trials and observational studies.
He is also an expert in the design, monitoring, and analysis of pragmatic cluster randomized trials, and is currently Principal Investigator of a Patient-Centered Outcome Research Institute (PCORI) methods award “New methods for planning cluster randomized trials to detect treatment effect heterogeneity”.
Dr. Li’s research interests include statistical methods for randomized clinical trials, observational studies and a combination of both. He is an expert in the design, monitoring, analysis of parallel-arm, crossover and stepped-wedge cluster randomized trials, which are increasingly seen in pragmatic clinical trials embedded in the health care delivery systems.
He has also contributed novel propensity score methods and software to estimate average causal effects with observational data, aimed at improving overlap and internal validity. His recent methods research include generalizability of randomized trials to external target populations, confirmatory or exploratory heterogeneity of treatment effects analyses, complex endpoints in cluster randomized trials, as well as novel study designs to address patient-centered clinical research questions. His methodological research has been supported by multiple NIH and PCORI grants/awards.
Meeting ID: 880 2368 7518
Password (if necessary): 696716