The NIH Office of Disease Prevention will continue its Methods: Mind the Gap webinar series on July 29 with “From Working Models to Reliable Evidence: Robust and Efficient Analysis of Cluster-Randomized Trials.” Dr. Bingkai Wang of the University of Michigan School of Public Health will lead the session.

From the announcement:

Cluster-randomized trials are essential for evaluating prevention, health care delivery, and policy interventions in real-world settings, but their analyses often rely on working models for which assumptions are difficult to verify. When these models are misspecified, standard covariate-adjusted analyses can lose efficiency, blur the target estimand, or introduce bias, especially when cluster sizes vary after randomization. This presentation will introduce a model-robust framework for estimating cluster-average and individual-average treatment effects in cluster-randomized trials. The presenter will show how familiar tools, including generalized estimating equations and mixed models, can be adapted using weighted g-computation to improve interpretability and robustness. There will then be a discussion of efficient estimators that allow flexible covariate adjustment, including machine learning, while maintaining valid inference and addressing informative cluster-size variation. Simulations and real trial examples will illustrate how these methods can strengthen the reliability, precision, and transparency of evidence from prevention research.

Wang is an assistant professor of biostatistics at the University of Michigan School of Public Health. He specializes in randomized clinical and cluster randomized trials and causal inference, with a focus on covariate adjustment, repeated measures analysis, and pragmatic trial design.

Register in advance to join the online presentation. Registration is required.