I am an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. I completed my PhD in Biostatistics at Harvard University and previously received a BScH in Mathematics from Queens University.
I’m interested in developing statistical tools to solve problems in epidemiology, environmental health, and health policy.
research
Selected methods papers:
Wen, L., & McGee, G. (2026). “Estimating average causal effects with incomplete exposure and confounders.” Journal of Causal Inference.
Pan, T., Shin, H. H., McGee, G., & Stringer, A. (2025). “Estimating associations between cumulative exposure and health via generalized distributed lag non-linear models using penalized splines.” Biometrics.
McGee, G., Coull, B. A., & Wilson, A. (2025). “Collapsible kernel machine regression for exposomic analyses.” Statistics in Medicine.
*McGee, G., & *Stringer, S. (*Equal contribution) (2024). “Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data.” Scandinavian Journal of Statistics.
McGee, G., Wilson, A., Coull, B. A., & Webster, T. F. (2023). “Incorporating biological knowledge in analyses of environmental mixtures and health.” Statistics in Medicine.
McGee, G., Wilson, A., Webster, T., and Coull, B. A. (2023). “Bayesian Multiple Index Models for Environmental Mixtures”. Biometrics.
McGee, G., Haneuse, S., Coull, B. A., Weisskopf, M., and Rotem R. (2022). “Outcome-Dependent Sampling in Cluster-Correlated Data Settings with Application to Hospital Profiling.” Epidemiology.
McGee, G., Perkins, N., Mumford, S., Kioumourtzoglou, M.-A., Weisskopf, M., Schildcrout, J., Coull, B., Schisterman, E., and Haneuse, S. (2020) “Methodological Issues in Population-Based Studies of Multigenerational Effects.” American Journal of Epidemiology.
McGee, G., Kioumourtzoglou, M.-A., Weisskopf, M., Haneuse, S., and Coull, B. (2020) “On the Interplay Between Exposure Misclassification and Informative Cluster Size in Multigenerational Studies.” Journal of the Royal Statistical Society: Series C.
McGee, G., Schildcrout, J., Normand, S.-L. and Haneuse, S. (2020). “Outcome-Dependent Sampling in Cluster-Correlated Data Settings with Application to Hospital Profiling.” Journal of the Royal Statistical Society: Series A.
Coull, B., Lee, S., McGee, G., Manjourides, J., Mittleman, M., and Wellenius, G. (2020). “Corrections for Measurement Error Due to Delayed Onset of Illness for Case-Crossover Designs.” Biometrics.
McGee, G., Weisskopf, M. G., Kioumourtzoglou, M. A., Coull, B. A., and Haneuse, S. (2020). “Informatively empty clusters with application to multigenerational studies”. Biostatistics.
See here for more details.
teaching
Advanced Biostatistics (STAT438)
University of Waterloo
Winter 2026
Fourth year undergraduate course. Calendar Description: Causal inference methodologies including propensity score matching and inverse probability weighting. Methods for handling incomplete data and covariate measurement error; likelihood based on joint models, estimating functions.
Analysis of Longitudinal Data (STAT936)
University of Waterloo
Winter 2025, Winter 2026
Graduate level course. Calendar Description: This course covers methods for analyzing data in which repeated measures have been obtained for individuals in health studies over time. Different methods will be discussed to handle both continuous and discrete longitudinal response data, with examples from biomedical and population health datasets. Some of the approaches covered will include linear, non-linear, and generalized linear mixed effects models, as well as generalized estimating equations and transition models, with distinctions drawn between subject-specific and population averaged approaches for generalized linear longitudinal response data. Also, there will be coverage of exploratory methods, evaluation of model assumptions and adapting to assumption violations, approaches for handling missing data, and treatment of advanced topics such as semiparametric and nonparametric models for longitudinal data. Software (e.g. R or SAS) will be used throughout the course.
Causal inference and Epidemiological Studies (STAT931)
University of Waterloo
Fall 2023, Fall 2024, Fall 2025
Graduate level course. Calendar Description: Causal inference in health research will be covered. Methods for the design and analysis of randomized controlled trials including randomization techniques, sample size and power calculations, and specialized additional topics including missing data, noncompliance, and ethics. The design and analysis of classical and modern epidemiological studies will then be discussed for settings in which randomization is not feasible. Causal inference methodologies for the analysis of observational data include propensity scores, marginal structural models and instrumental variables. Studies will be discussed from the epidemiological literature and other sources in the public domain. Simulations and data analyses will be carried out using software (e.g. R or SAS). Students will be trained and assessed in part based on the preparation of reports and delivery of presentations.
Generalized Linear Models and their Applications (STAT431/STAT831)
University of Waterloo
Fall 2023, Spring 2023, Spring 2022
Fourth year undergraduate course (431) taught jointly with graduate course (831). Calendar Description: Review of normal linear regression and maximum likelihood estimation. Computational methods, including Newton-Raphson and iteratively reweighted least squares. Binomial regression; the role of the link function. Goodness-of-fit, goodness-of-link, leverage. Poisson regression models. Generalized linear models. Other topics in regression modelling.
Applied Linear Models (STAT331/SYDE334)
University of Waterloo
Fall 2022, Spring 2021, Winter 2021
Third year undergraduate course. Calendar Description: Modeling the relationship between a response variable and several explanatory variables (an output-input system) via regression models. Least squares algorithm for estimation of parameters. Hypothesis testing and prediction. Model diagnostics and improvement. Algorithms for variable selection. Nonlinear regression and other methods.
Introductory Probability
Harvard T.H. Chan School of Public Health
Summer 2018, Summer 2017
Month-long preparatory course for incoming biostatistics doctoral students. Topics: Set theory, Kolmogorov’s axioms, combinatorics, conditional probability, independence, Bayes theorem, moments, functions of random variables.
cv
Download my CV here.