University teaching and conference workshops in biostatistical methods, practice, and computing
University Teaching
Spring 2020–2025 · R examples and case studies
Methods for time-to-event data, with case studies and hands-on analysis in R.
- Kaplan–Meier estimation, log-rank tests and Cox regression
- Recurrent events, competing risks, joint models and multistate processes
- Composite endpoints, causal inference and machine learning
Course website ↗
Spring 2019 · 24 lectures · SAS examples
Methods for repeated measurements and correlated outcomes in the health sciences.
- Response profiles, mean and covariance structures
- Mixed-effects models, GEE and generalized linear mixed models
- Missing data, smoothing and multilevel models
Course website ↗
2017 · Seven chapters and lecture slides
Statistical inference for incomplete data, from likelihood to semiparametric methods.
- Missingness mechanisms and identifiability
- EM algorithms, regression with missing covariates, imputation and Bayesian analysis
- Inverse probability weighting and doubly robust estimation
Course website ↗
Conference Workshops
Applying R’s Tidyverse to Survival Data
JSM 2025
Tidy R workflows for survival analysis, from data preparation to predictive modeling.
- Data preparation, visualization and summary tables
- Survival and competing risks, Cox regression and diagnostics
- Machine learning for survival outcomes with tidymodels
Workshop website ↗
Win Ratio and Beyond
NESS 2025
Methods for prioritized composite endpoints in clinical trials.
- Win ratio testing and sample size calculation
- Restricted win ratio, average win time and while-alive loss rate
- Proportional win-fractions regression and risk prediction
Workshop website ↗