Dr. Schnell's research focuses on using Bayesian hierarchical and linear mixed effects models to address problems in causal inference in public health and medicine. Most problems he tackles relate to subgroup analysis in randomized experiments, both traditional with respect to baseline variables and principal stratification with respect to post-randomization variables. Other areas of interest include theory of causal inference with hierarchical and mixed effects models in spatial data, and infectious disease epidemiology.
Bayesian hierarchical models, linear mixed effects models, clinical trials, causal inference, subgroup analysis, principal stratification, spatial data, infectious disease epidemiology
- Ph.D., University of Minnesota Division of Biostatistics, 2017
- B.Sc., Mathematics, The Ohio State University, 2013
Schnell, PM, Wascher, M, Rempala, GA. "Overcoming repeated testing schedule bias in estimates of disease prevalence." Journal of the American Statistical Association, online ahead of print, 2023.
Schnell, PM. "Controlling the false-discovery rate when identifying the subgroup benefiting from treatment." Clinical Trials 20(4): 394–404, 2023.
Schnell, PM, Baumgartner, R, Mt-Isa, S, Svetnik, V. "A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes." Journal of the Royal Statistical Society Series C: Applied Statistics 71(5): 1065–1084, 2022.
Schnell, PM. "Monte Carlo approaches to frequentist multiplicity-adjusted benefiting subgroup identification." Statistical Methods in Medical Research 30(4): 1026–1041, 2021.
Schnell, PM, Papadogeorgou, G. "Mitigating unobserved spatial confounding when estimating the effect of supermarket access on cardiovascular disease deaths." Annals of Applied Statistics 14(4): 2069–2095, 2020.