9 citations · 9 across the 3 of their papers we have counts for
4 papers · 1 filter
Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: a simulation study
Hongxiang Qiu, Andrea J. Cook, Jennifer F. Bobb
Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce ele…
Accounting for spatial confounding in epidemiological studies with individual-level exposures: An exposure-penalized spline approach
Jennifer F. Bobb, Maricela F. Cruz, Stephen J. Mooney +3
In the presence of unmeasured spatial confounding, spatial models may actually increase (rather than decrease) bias, leading to uncertainty as to how they should be applied in prac…
Clarifying Selection Bias in Cluster Randomized Trials: Estimands and Estimation
Fan Li, Zizhong Tian, Jennifer Bobb +1
In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads…
Bayesian kernel machine regression-causal mediation analysis
Katrina L. Devick, Jennifer F. Bobb, Maitreyi Mazumdar +7
Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural…