Curriculum

The Department of Biostatistics and Computational Biology offers a variety of graduate-level BST courses through the School of Medicine and Dentistry for students in the Statistics PhD, Statistics MA, Biostatistics MS, and other programs. Undergraduate-level STAT courses are offered through the School of Arts and Sciences.

• Graduate Course Schedules
• BST Course List
• BST Course Descriptions
• BST Sample Syllabi

Graduate Course Schedules

Department of Biostatistics and Computational Biology Course Schedules (BST courses)

Fall 2024    Spring 2025 

Past Schedules: Spring 2024 | Fall 2023 | Spring 2023 | Fall 2022 | Spring 2022 | Fall 2021 | Spring 2021 | Fall 2020 | Spring 2020 and older schedules

University of Rochester Course Schedules (All course offerings, including IND courses)

• Course Description / Course Schedule System

BST Course List

 

BST Course Descriptions

Unless otherwise noted all courses carry four credit hours.

BST 401 Probability Theory

Prerequisite: MATH 265 or equivalent (or permission).

Probability spaces; random variables; independence; distributions; expectation; characteristic functions and inversion theorems; convergence; laws of large numbers; central limit theorem.

BST 402 Stochastic Processes

Prerequisite: BST 401.

Markov chains; birth-death processes; random walks; renewal theory; Poisson processes; Brownian motion; branching processes; martingales; with applications.

BST 411 Statistical Inference I

Prerequisite: STAT 203 or equivalent.

Probability distributions, transformations and sampling distributions; statistical models; estimation, hypothesis testing, and confidence intervals for parametric models; introduction to large-sample methods.

BST 412 Statistical Inference II

Prerequisites: BST 401 and BST 411.

Types of convergence; asymptotic linearity; influence functions; consistency and asymptotic normality; large sample estimation, maximum likelihood estimation; Wald, likelihood ratio, and score tests; generalized Neyman-Pearson lemma; nuisance parameters; efficiency; alternative methods for estimation (M-estimation, GEE, generalized method of moments); resampling methods (bootstrap, permutation tests); decision-theoretic inference.

BST 413 Bayesian Inference

Prerequisite: BST 411.

Posterior distributions for single and multiple parameter models under conjugacy; hierarchical models; noninformative and informative prior distributions; modern computational techniques, including Markov chain Monte Carlo; model checking; posterior predictive checks; sensitivity analysis.

BST 426 Linear Models

Prerequisites: STAT 203 and MATH 235.

Theory of least-squares; point estimation in the general linear model; projection operators, estimable functions and generalized inverses; tests of general linear hypotheses; power; confidence intervals and ellipsoids; simultaneous inference; linear and polynomial regression; analysis of variance and analysis of covariance models; fixed, random, and mixed effects; correlation; prediction.

BST 430 Introduction to Statistical Computing

Prerequisite: BST 411 (or co-enrollment).

Basic/intermediate R programming; statistical analysis in R; visualization in R; reproducible research and collaborative coding; command line tools and BlueHive. Introduction to SAS programming; statistical analysis in SAS. Topics in statistical analysis provide working examples.

BST 432 High Dimensional Data Analysis

Prerequisites: BST 401 and BST 411.

An overview of modern tools for high-dimensional data analysis, with a particular focus on connecting them to their statistical underpinnings, both applied and theoretical perspectives. Emphasis will be placed on understanding benefits and limitations of these tools. The major topics include: decision theory; basic tail and concentration bounds; univariate/multivariate methods; large-scale testing; penalized methods; dimension reduction; clustering; tree-based methods; support vector machine; network analysis.

BST 433 Computational Systems Biology

Prerequisites: BST 401 and BST 411.

Overview of the relevant molecular and cellular biology; overview of bioinformatic data, including high-throughput platforms, annotation, ontology, and pathway databases; introduction to Bioconductor; gene set enrichment analysis; supervised and unsupervised machine learning; network models (including Bayesian networks and Boolean networks); protein-protein interaction networks; dynamic models and time-course data.

BST 434 Genomic Data Analysis

Prerequisite: BST 411.

Introduction to techniques used in modern genomic experimentation and the corresponding statistical methods and software available to visualize, analyze, and interpret these data. Specific topics include mRNA/microRNA expression, protein abundance, protein-DNA binding, copy number variants, single nucleotide variants, DNA methylation, and microbial abundance.

BST 450 Data Analysis

Prerequisites: BST 426 and BST 430.

Statistical analysis of data under nonstandard conditions; examination of adequacy of model assumptions; goodness-of-fit testing; transformations; robust inference.

BST 452 Design of Experiments

Prerequisites: BST 426 and BST 430.

Completely randomized designs; replication; covariate adjustment; randomized block designs; fixed vs. random effects; Latin and Graeco-Latin squares; confounding; nesting; factorial and fractional factorial designs; split-plot designs; incomplete block designs; response surfaces.

BST 461 Biostatistical Methods I

Prerequisite: BST 463 or equivalent.

Study designs; inference regarding proportions; contingency table analysis; diagnostic testing; one-way and two-way analysis of variance; multiple comparisons involving means; simple and multiple linear regression; analysis of covariance; interactions; logistic and Poisson regression; introduction to survival analysis; multicollinearity; variable selection; model checking; sample size determination.

BST 462 Biostatistical Methods II

Prerequisite: BST 461 or equivalent.

Linear mixed effects models; intraclass correlation; advanced logistic regression; generalized estimating equations; missing data; extensions of the Cox proportional hazards model; shrinkage estimation in regression; nonparametric methods; bootstrap methods; scatterplot smoothing; nonparametric regression (trees, forests, generalized additive models).

BST 463 Introduction to Biostatistics

Credits: 3

Introduction to statistical techniques with emphasis on applications in the health sciences. Summarizing and displaying data; introduction to probability; Bayes’ theorem and its application in diagnostic testing; binomial, Poisson, and normal distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing involving means and proportions; simple correlation and regression; contingency tables; use of statistical software.

BST 465 Design of Clinical Trials

Prerequisite: BST 463 or equivalent.

Introduction to the principles of clinical trials; clinical trial protocols; overview of the drug development process; hypotheses/objectives; specification of response variables; defining the study population; randomization; blinding; ethical issues; factorial designs; crossover designs; equivalence trials; trial monitoring and interim analyses; sample size and power; issues in data analysis and reporting; evaluating clinical trial reports.

BST 467 Applied Statistics in the Biomedical Sciences

Credits: 3

Introduction to statistical techniques with emphasis on applications in the biomedical sciences. Introduction to probability and probability distributions; sampling distributions; estimation, confidence intervals, and hypothesis testing in small and large samples; analysis of categorical data; analysis of variance; correlation and linear and nonlinear regression analysis; use of statistical software; illustrations using published articles in the biomedical sciences.

BST 479 Generalized Linear Models

Prerequisites: BST 411 and BST 426.

Generalized linear models; computational techniques for model fitting; logistic and conditional logistic regression; Poisson and negative binomial regression; log-linear models; models for nominal and ordinal categorical data; quasi-likelihood functions; model checking; nonlinear regression models.

BST 487 Seminar in Statistical Literature

Credits: 1

Provides an introduction to the process of searching the statistical literature, opportunities to acquire knowledge of a focused area of statistical research, experience in organizing, preparing, and delivering oral presentations, and an introduction to the research interests of members of the faculty.

BST 491 Reading Course at the Master’s Level

Credits: Varies

Special work, arranged individually.

BST 493 Capstone Project

Credits: 8

As required for completion of the MS degree in biostatistics, the student works on a medical research project under the guidance of department faculty. The student should have contact with medical investigators as well as statisticians. The work should be coherently summarized in a written document. Oral presentation of the work is required.

BST 495 Research at the Master’s Level

Credits: Varies

BST 511 Topics in Statistical Inference I

Prerequisite: Varies by topic.

Advanced topics in statistical inference and/or decision theory.

BST 512 Topics in Statistical Inference II

Prerequisite: Varies by topic.

Advanced topics in statistical inference and/or decision theory.

BST 513 Analysis of Longitudinal and Dependent Data

Prerequisites: BST 401, BST 411, and BST 426.

Modern approaches to the analysis of longitudinal and dependent data; random and mixed effects models; marginal models; generalized estimating equations; models for continuous and discrete outcomes.

BST 514 Survival Analysis

Prerequisites: BST 401, BST 411, and BST 412.

Parametric, nonparametric, and semiparametric methods for the analysis of survival data; right censoring; Kaplan-Meier curves; log-rank and weighted log-rank tests; survival distributions; accelerated life and proportional hazards regression models; time-dependent covariates; partial likelihood; models for competing risks and multiple events.

BST 516 Causal Inference

Prerequisites: BST 401, BST 411, and BST 412.

Statistical methods for the design and analysis of observational studies; potential outcomes framework for causal inference; randomized experiments; matching, propensity score and regression methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; instrumental variables. 

BST 523 Advanced Bayesian Inference

Prerequisite: BST 413.

Computational aspects of Bayesian Inference, with a focus on the Metropolis-Hastings (MH) algorithm and its extensions; convergence diagnostics; missing data and data augmentation; simulation design; Hamiltonian Monte Carlo; hierarchical models; use of R and Stan software. 

BST 531 Nonparametric Inference

Prerequisites: BST 411, BST 412, and BST 426.

Nonparametric estimation and inference for one-sample location and paired data, two-sample location and/or dispersion, one- and two-way layouts with and without order restrictions, tests of independence, and regression; exact and large-sample results for some commonly used procedures, including the sign test and the sample median, the Mann-Whitney-Wilcoxon test and the Hodges-Lehmann location measure, and some generalizations to more complex data structures; density estimation; nonparametric regression; generalized additive models (GAM); cross-validation; bandwidth selection; exact and asymptotic bias, variance, and mean squared error (MSE).

BST 536 Sequential Analysis

Prerequisite: BST 412.

The Wald sequential probability ratio test and generalizations; tests of composite hypotheses; nonparametric sequential procedures; sequential estimation and confidence intervals; Brownian-motion based sequential methods, with applications to clinical trials; group sequential methods; optimal stopping rules.

BST 541 Multivariate Analysis

Prerequisites: BST 411 and BST 426.

Multivariate normal and Wishart distributions and associated distributions; estimation; invariance reduction; Hotelling’s T²; multivariate general linear model; simultaneous confidence bounds; step down procedures; optimality properties; classification; discrimination; principal components.

BST 550 Topics in Data Analysis

Restriction: Permission of instructor.

Advanced statistical methods for data analysis.

BST 570 Topics in Biostatistics

Restriction: Permission of instructor.

Advanced biostatistical techniques.

BST 582 Introduction to Statistical Consulting

Restriction: Permission of instructor.

Formal instruction on developing and managing consulting relationships.

BST 590 Supervised Teaching

Credits: Varies

One to two classroom hours per week of discussion and problem solving with University of Rochester students, under the guidance of a member of the faculty.

BST 591 Reading Course at the PhD Level

Credits: Varies

Special work for doctoral candidates, arranged individually.

BST 592 Supervised Statistical Consulting

Credits: Varies

Supervised consulting with medical and other scientific researchers under the guidance of a member of the faculty.

BST 595 Research at the PhD Level

Credits: Varies