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This interactive, discussion-based course is based on the principles of the American Statistical Association’s . This course seeks to help students develop an ethical reasoning framework in order to understand ethical dilemmas occurring in biomedical research. Cases may be used to explore issues surrounding professional integrity and accountability, integrity of data and methods, responsibilities to stakeholders, and ethical misconduct. 0 credits. Spring 2027 (Davidson)

This course is designed for students who seek to develop skills in statistical computing, focusing on fundamental concepts and practices in statistical computing for data analysis and research. Students will develop computational thinking and programming skills using R and modern computing environments. Core topics include data structures, programming logic, simulation studies, and AI-assisted approaches to statistical computing. Additional topics include reproducible research workflows using R Markdown, version control with GitHub, parallel computing, and an introduction to Python. 2 credits. Fall 2026 (S. Zhao)

This course is the first in a two-course sequence on fundamental principles of biostatistics. Students will learn the statistical principles that govern analysis of data in biomedical research, focusing on principles of statistical inference and their application to data analysis techniques. Students will learn how to summarize and interpret data, and will learn how statistical tools are used toward evidence-based decision making. Topics include principles of probability, common distributions and their characteristics, bias and variability, introduction to large sample theory (e.g., the law of large numbers and the central limit theorem), estimation principles for means and proportions, an introduction to nonparametric statistics, the basics of simulation studies, and an introduction to resampling techniques (e.g., bootstrapping). The frequentist, Bayesian, and likelihood paradigms for statistical inference are all introduced. This course emphasizes implementation and interpretation over theory and computation. 4 credits. Fall 2026 (Amorim)

This course is the second in a two-course sequence on fundamental principles of biostatistics. Students will learn modern regression methods used to analyze biomedical and epidemiologic data, with an emphasis on practical model implementation and interpretation. Topics include simple and multiple linear regression, analysis of variance models, and introductory regression approaches for binary, ordinal, count, and time-to-event outcomes. Students will learn strategies for model building, evaluating confounding and effect modification, assessing model fit and assumptions using applied diagnostics, and communicating regression results effectively. Both frequentist and Bayesian approaches to regression modeling are covered, with emphasis on interpretation and applied use. Additional topics include flexible modeling with splines, basic approaches to model validation, and introductory methods for handling missing data. This course emphasizes application and interpretation, with theory introduced only as needed for understanding model behavior. Prerequisite: BIOS 6309. 4 credits. Spring 2027 (Slaughter)

This course is the first in a two-course sequence on modern biostatistical methodology. Students will learn the statistical principles that govern analysis of data in biomedical research, focusing on principles of statistical inference and their application to data analysis techniques. Students will learn classical statistical theory and modern computational approaches, integrating mathematical derivations with applications toward evidence-based decision making. Topics include principles of probability; common distributions and their characteristics; bias and variability; introduction to large sample theory (e.g., the law of large numbers and the central limit theorem); estimation principles for means and proportions; an introduction to nonparametric statistics; Bayesian estimation with prior construction, conjugate families, and posterior-based inference; using simulation studies to evaluate operating characteristics; and computational approaches to inference including bootstrapping and permutation tests. This course emphasizes implementation, interpretation, theory, and computation in approximately equal parts. 4 credits. Fall 2026 (Amorim)

This course is the second in a two-course sequence on modern biostatistical methodology. Students will learn modern regression modeling and model-building techniques from both applied and theoretical perspectives. Topics include simple and multiple linear regression, analysis of variance models, and introductions to regression methods for binary, ordinal, count, and time-to-event outcomes. Mathematical and computational foundations of these models are integrated with applications, including matrix formulations of linear models, distributional assumptions, and properties of estimators. Both frequentist and Bayesian frameworks for regression modeling are covered, including conceptual foundations and their implications for inference. Additional topics include flexible modeling with splines, data-reduction techniques, model validation, and an introduction to methods for handling missing data. This course emphasizes implementation, interpretation, theory, and computation in approximately equal parts. Prerequisite: BIOS 6311. 4 credits. Spring 2027 (Slaughter)

This course is an introduction to statistical aspects of the design, monitoring, and analysis of experiments. Emphasis is on studies of human subjects (i.e., clinical trials). Topics include study designs, randomization and balance, selection of estimands and endpoints, sample size projections, data collection and quality control, data monitoring and interim analysis, principles of and issues in the analysis of trial data, and interpretation and reporting of results. Prerequisite: BIOS 6311; BIOS 6312 is recommended as a co-requisite. 3 credits. Spring 2027 (Blette)

This course is the first in a two-course series on probability and statistical inference that introduces and explores the probabilistic framework underling statistical theory. Students learn probability theory—the formal language of uncertainty—and its application to everyday statistical concepts and analysis methods. Topics include probability axioms, probability and sample space, events and random variables, transformation of random variables, probability inequalities, independence, discrete and continuous distributions, expectations and variances, conditional expectation, moment generating functions, random vectors, convergence concepts (in probability, in distribution, and almost surely), weak and strong law of large numbers, central limit theorem, delta method, order statistics, and exponential family. 4 credits. Fall 2026 (Ma)

This course is the second in a two-course series on probability and statistical inference that introduces and explores the fundamental inferential framework for parameter estimation, testing hypotheses, and interval estimation. Students learn classical methods of inference (hypothesis testing), and paradigms of statistical inference (frequentist, Bayesian, likelihood) and their surrounding controversies. Topics include sufficiency, minimal sufficiency, exponential family, ancillarity, completeness, conditionality principle, Fisher’s information, Cramer-Rao inequality, hypothesis testing (likelihood ratio test, most powerful test, optimality, Neyman-Pearson lemma, inversion of test statistics), likelihood principle, law of likelihood, Bayesian posterior estimation, interval estimation (confidence intervals, support intervals, credible intervals), basic asymptotic and large sample theory, and maximum likelihood estimation. Prerequisite: BIOS 6341. 4 credits. Spring 2027 (Spieker)

This introductory course prepares first-year graduate students in biostatistics for effective participation in interdisciplinary research teams in the health sciences and serves as the foundation for the program’s advanced collaboration course. Emphasizing the central role of biostatisticians in team science, the course focuses on developing the habits of mind and communication skills essential to professional biostatistical practice and scholarship. Students learn to critically engage with clinical and statistical literature; contribute to grant proposals; write structured and unstructured abstracts; and prepare clear, reproducible analysis reports. Training also includes the development of effective slide and poster presentations and strategies for navigating authorship, roles, and responsibilities within collaborative research teams. Attention is given to career pathways in academia, industry, and government. Throughout, emphasis is placed on rigorous statistical reasoning, transparency, reproducibility, and the ethical conduct of collaborative scientific research. Prerequisite: BIOS 6311; BIOS 6312 is recommended as a co-requisite. 2 credits. Spring 2027 (Samuels)

This course provides an applied introduction to methods for time-to-event data with censoring mechanisms. Topics include life tables, non-parametric approaches (Kaplan-Meier curves, log-rank test), semi-parametric approaches (Cox proportional hazards model), parametric approaches (Weibull, gamma), competing risks, and time-dependent covariates. Focus is on fitting the models and the relevance of those models for the biomedical application. Prerequisites: BIOS 6312, BIOS 6342. 3 credits. Fall 2026 (Tao)

This course presents strategies for, and a survey of current thinking on, building multivariable regression models primarily for the purpose of prediction, but also for estimation and inference. Topics include using regression splines to relax linearity assumptions, the perils of variable selection and over-fitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction, and interaction surfaces. There is also an emphasis placed on describing approaches for graphically understanding models and using resampling to estimate a model’s likely performance on new data. Statistical methods related to binary logistic models, ordinal logistic models, and survival models are covered. Students will develop, validate, and graphically describe multivariable regression models. Prerequisite: BIOS 6312. 3 credits. Not offered 2026-2027.

This course covers the methodology and rationale for Bayesian methods and their applications. Topics include the historical development of Bayesian methods such as hierarchical models, Markov Chain Monte Carlo (MCMC) and related sampling methods, specification of priors, sensitivity analysis, and model checking and comparison. This course features applications of Bayesian methods to biomedical research. Prerequisites: BIOS 6301, BIOS 6342. 3 credits. Spring 2027 (Hackstadt)

This course is the first in a two-course sequence on intermediate regression methodology. The first half of the course covers ordinary and weighted least squares, analysis of variance (ANOVA), hypothesis testing, confidence and prediction regions, model misspecification, leverage, influence, and diagnostics. The second half of the course covers generalized linear models (e.g., binomial, Poisson, and gamma regression) from the likelihood and Z-estimation frameworks, overdispersion and quasi-likelihood, hypothesis testing, diagnostics, and negative binomial regression. This course emphasizes methodology and computational exploration more than mathematically technical details and theoretical underpinnings.ÌýPrerequisites: BIOS 6312, BIOS 6342. 3 credits. Fall 2026 (Spieker)

This course is the second in a two-course sequence on intermediate regression methodology. This course extends linear and generalized linear models to the analysis of correlated and longitudinal data commonly arising in biomedical and public health research. Core topics include generalized least squares, likelihood-based mixed-effects models, and semiparametric methods such as generalized estimating equations (GEE). Additional topics include transition models, marginalized models, and handling of missing data. Emphasis is placed on practical modeling strategies, interpretation of subject-specific and population-averaged effects, estimation procedures, model diagnostics, and computational implementation. The course focuses primarily on methodology and applied problem solving, with attention to statistical properties at a conceptual level rather than detailed theoretical derivations. Prerequisite: BIOS 7345. Spring 2027 (J. Liu)

In this course, students are exposed to a variety of challenges that arise in collaborative arrangements. The course’s goal is to sharpen students’ collaborative skills while exposing them to the application of advanced statistical techniques in routine health science applications. The importance of understanding and learning the science underlying collaborations are emphasized. Students are exposed to real collaborative projects, and face real-life challenges such as opaque scientific direction, poor scientific formulation, lack of time, and ill-formulated messy data. Students engage in several projects that involve the use of a wide range of biostatistics methods from design to analysis. Course content may also make use of departmental clinics. Prerequisites: BIOS 6312, BIOS 6351. 3 credits. Spring 2027 (Xiang)

This course introduces the foundational principles of probabilistic machine learning and modern statistical modeling, emphasizing the mathematical structure underlying common learning algorithms. Topics include discriminant analysis, kernel methods, tree-based methods, neural networks, dimensionality reduction, and clustering. Students will develop a unified probabilistic perspective on machine learning grounded in probability theory, statistical inference, decision theory, information theory, and optimization. The course balances theoretical understanding with practical modeling insight, preparing students to critically evaluate and apply machine learning methods in biomedical and data science contexts. Assignments focus on mathematical reasoning, conceptual clarity, and interpretation of modeling assumptions. Biomedical case studies are used to illustrate real-world deployment of statistical learning algorithms. Prerequisite: BIOS 6342. Fall 2026 (Asiaee)

This advanced course introduces causal inference methods for observational data and randomized studies. Topics include the Rubin causal model, directed acyclic graphs, propensity scores, inverse probability weighting, instrumental variables, causal mediation analysis, marginal structural models, g-computation, and sensitivity analyses to examine robustness to untestable assumptions. Students learn the basic theory behind the methods and their application to biomedical data examples. Prerequisites: BIOS 6312, BIOS 6342. 3 credits. Not offered 2026-2027.

Designed to allow the student to explore and/or master advanced or specialized topics in biostatistics under the guidance of faculty with relevant expertise. 3 credits. Mentor-specific enrollment.

This course is the first in a two‑course sequence on advanced regression methodology. The first half of the course covers ordinary and weighted least squares, regression-based limit theorems, analysis of variance (ANOVA), hypothesis testing, confidence and prediction regions, model misspecification, and diagnostics. The second half of the course covers generalized linear models (e.g., binomial, Poisson, and gamma regression) from the likelihood and Z-estimation frameworks, overdispersion and quasi-likelihood, hypothesis testing, diagnostics, and negative binomial regression. Gauss-Markov optimality is a central theme of this course. As time permits, advanced topics may be featured such as regularization, nonlinear least squares, cumulative probability models, receiver operating characteristic regression, and matched pair designs. This course emphasizes methodology, computational exploration, and theoretical underpinnings in roughly equal parts. Prerequisites: BIOS 6312, BIOS 6342. 3 credits. Fall 2026 (Spieker)

This course is the second in a two-course sequence on advanced regression methodology. This course develops the theoretical and methodological foundations of regression models for correlated and longitudinal data. Topics include generalized least squares, likelihood-based mixed-effects models, generalized estimating equations (GEE), transition and marginalized models, missing data mechanisms, and inference under incomplete data. Connections between likelihood-based and estimating equation-based semiparametric inference frameworks are emphasized. The course examines asymptotic theory for correlated data, efficiency considerations, model specification and identifiability, and the theoretical properties of estimators under dependence. Advanced topics may include semiparametric efficiency, robustness under misspecification, and modern extensions relevant to contemporary biomedical research. The course places substantial emphasis on theoretical development alongside methodological and computational components. Prerequisite: BIOS 8345. 3 credits. Spring 2027 (J. Liu)

This course is the first in a two-course series on advanced probability and statistical inference. Topics include characteristic functions, modes of converge, uniform integrability, Brownian motion, classical limit theorems, L^p spaces, projections, sigma-algebras and RVs, martingales, random walks, Markov chains, and probabilistic asymptotics. Emphasis on measure theory is minimal. Concepts are illustrated in biomedical applications whenever possible. Prerequisite: BIOS 6342. 3 credits. Fall 2026 (Zhang)

This course introduces advanced computing and analytical skills and concepts in biomedical data sciences. The course covers real-world data platforms, diverse analytics environments, statistical data analysis in Python, numerical methods, bioinformatics computing, cloud computing, and data visualization. The objective of the course is to enable students to adapt to the computational challenges required by today’s biomedical data sciences and big data computing, and to equip them with the knowledge and skills to solve real-world problems. Students are expected to analyze big complex data in cloud-based computing and data analysis environments. The course involves substantial programming in R, Python, SQL, and shell scripts. Prerequisites: BIOS 6301, BIOS 6342. 3 credits. Not offered 2026-2027.Ìý

This course presents advanced topics in design, analysis, and governance of clinical trials. Design topics include adaptive trials, pragmatic trials, sequential trials, cluster-randomized trials, and platform trials. Analysis topics include methods for handling missing data, estimand frameworks, Bayesian analysis methods, composite endpoints, and confirmatory and exploratory assessment of heterogeneous treatment effects. Governance topics include regulatory perspectives on advanced trial designs, informed consent in pragmatic trials, and other ethical and operational issues. Prerequisites: BIOS 6321, BIOS 6342. 3 credits.ÌýNot offered 2026-2027

This course covers standard and modern/advanced methods for neuroimage analysis from a biostatistical perspective. Students will learn to analyze and interpret common modalities such as fMRI, structural MRI, cortical thickness, diffusion-weighted imaging, and resting-state connectivity using popular neuroimaging analysis software and visualization tools. Advanced topics may include, site correction, first-level and group-level models, network analysis, AI/machine learning, circularity analysis, multivariate/spatial inference, confidence set methods, and centile methods. Upon completion of this course, students will be prepared to understand and contribute to statistical research in neuroimaging. Prerequisites: BIOS 6312, BIOS 6342. 3 credits.ÌýNot offered 2026-2027