1 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Evaluating Informative Cluster Size in Cluster Randomized Trials
Bryan S. Blette, Zhe Chen, Brennan C. Kahan +3
In cluster randomized trials, the average treatment effect among individuals (i-ATE) can be different from the cluster average treatment effect (c-ATE) when informative cluster siz…
An overview of methods for receiver operating characteristic analysis, with an application to SARS-CoV-2 vaccine-induced humoral responses in solid organ transplant recipients
Nathaniel P. Dowd, Bryan Blette, James D. Chappell +2
Receiver operating characteristic (ROC) analysis is a tool to evaluate the capacity of a numeric measure to distinguish between groups, often employed in the evaluation of diagnost…
Addressing Confounding and Continuous Exposure Measurement Error Using Corrected Score Functions
Brian D. Richardson, Bryan S. Blette, Peter B. Gilbert +1
Confounding and exposure measurement error can introduce bias when drawing inference about the marginal effect of an exposure on an outcome of interest. While there are broad metho…
Demystifying estimands in cluster-randomised trials
Brennan C Kahan, Bryan Blette, Michael Harhay +4
Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned to the study's objectives. Cluster randomised trials require additional at…
Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials
Bryan S. Blette, Scott D. Halpern, Fan Li +1
Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treat…