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Curriculum

• BST 452 Design of Experiments
• BST 463 Introduction to Biostatistics
• BST 464 Applied Linear Regression
• BST 465 Design of Clinical Trials
• BST 466 Categorical Data Analysis
• BST 467 Applied Statistics in the Biomedical Sciences
• BST 476 Introduction to Linear Models
• BST 479 Generalized Linear Models
• BST 487 Seminar in Statistical Literature
• BST 491 Reading Course at the Master's Level
• BST 493 Internship/Applied Project
• BST 495 Research at the Master's Level
• BST 496 Wet/Dry Lab Rotation
• BST 511 Topics in Statistical Inference I
• BST 512 Topics in Statistical Inference II
• BST 513 Analysis of Longitudinal and Dependent Data
• BST 514 Survival Analysis
• BST 531 Nonparametric Inference
• BST 536 Sequential Analysis
• BST 541 Multivariate Analysis
• BST 550 Topics in Data Analysis
• BST 570 Topics in Biostatistics
• BST 582 Introduction to Statistical Consulting
• BST 590 Supervised Teaching
• BST 591 Reading Course at the PhD Level
• BST 592 Supervised Statistical Consulting
• BST 595 Research at the PhD Level

Full Descriptions

BST 401 Probability Theory

Prerequisites:
MTH 265 or equivalent (or permission)
Description:
Probability spaces; random variables; independence; distributions; expectation; characteristic functions and inversion theorems; convergence; laws of large numbers; central limit theorem.
Offered:
Fall

BST 402 Stochastic Processes

Prerequisites:
BST 401
Description:
Markov chains; birth-death processes; random walks; renewal theory; Poisson processes; Brownian motion; branching processes; martingales; with applications.
Offered:
Fall

BST 411 Statistical Inference

Prerequisites:
STT 203 or equivalent
Description:
Probability distributions, transformations and sampling distributions; statistical models; estimation, hypothesis testing, and confidence intervals for parametric models; introduction to large-sample methods.
Offered:
Fall

BST 412 Large-Sample Theory and Methods

Prerequisites:
BST 401 and BST 411
Description:
Weak convergence; asymptotic linearity; local analysis; large sample estimation, maximum likelihood estimation and M-estimation; Wald, likelihood ratio, and score tests; confidence regions; nuisance parameters; efficiency; multinomial chi-square tests.
Offered:
Spring

BST 413 Bayesian Inference

Prerequisites:
BST 411
Description:
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.
Offered:
Fall

BST 416 Applied Statistics

Prerequisites:
STT 211 or STT 212 or BST 463 or equivalent
Description:
One- and two-way analysis of variance; simple and multiple regression; analysis of covariance; analysis of residuals, use of transformations; topics from contingency table analysis and nonparametric statistics. Emphasis on real examples from the biomedical and social sciences, with extensive use of statistical software.
Offered:
Spring

BST 421 Sampling Techniques

Prerequisites:
STT 203 or STT 213
Description:
Simple random, stratified, systematic, and cluster sampling; estimation of the means, proportions, variance, and ratios of a finite population. Ratio and regression methods of estimation and the use of auxiliary information. The nonresponse problem.
Offered:
Fall

BST 422 Design of Experiments

Prerequisites:
BST 416 or BST 464 or BST 476
Description:
Basic designs and their principles; randomization; blocking; use of concomitant information.
Offered:
Spring

BST 426 Linear Models

Prerequisites:
STT 203 and MTH 235
Description:
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.
Offered:
Spring

BST 430 Introduction to Statistical Computing

Prerequisites:
BST 411 (or co-enrollment)
Description:
Basic/intermediate R programming; statistical analysis in R; visualization in R; introduction to SAS programming; statistical analysis in SAS; reproducible research and collaborative coding; command line tools and BlueHive. Topics in statistical analysis provide working examples.
Offered:
Fall

BST 432 High Dimensional Data Analysis

Prerequisites:
BST 401 and BST 411
Description:
Application of statistical theory to the analysis of high throughput data; introduction to Bioconductor; molecular profiles (mRNA, cDNA, microRNA, proteomics); platforms (Affymetrix and other microarrays, PCR, RNA seq); quality control (quality assessment, batch-effects); exploratory methods (graphical methods, clustering, principal component analysis and other dimension reduction techniques); differential expression and multiple hypothesis testing; classification (feature selection, multivariate methods, machine learning, cross-validation).

BST 433 Computational Systems Biology

Prerequisites:
BST 401 and BST 411
Description:
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 441 Applied Multivariate Analysis

Prerequisites:
BST 476 or BST 426
Description:
Methodology and applications of multivariate analysis; Hotelling's T2; multivariate regression and analysis of variance; classification and discrimination; principal components, clustering, and multidimensional scaling; use of statistical software.
Offered:
Spring

BST 450 Data Analysis

Prerequisites:
BST 426 and BST 477or BST 478
Description:
Statistical analysis of data under nonstandard conditions; examination of adequacy of model assumptions; goodness-of-fit testing; transformations; robust inference.

BST 451 Exploratory Data Analysis

Prerequisites:
BST 416 or BST 476 and BST 478
Description:
Graphical techniques to reveal structure in data; model fitting to describe structure; model checking; transformations; outliers and resistant fitting methods.

BST 452 Design of Experiments

Prerequisites:
BST 426 and BST 477 or BST 478
Description:
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 463 Introduction to Biostatistics

Restrictions:
Permission of instructor required for undergraduates
Description:
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.
Offered:
Fall

BST 464 Applied Linear Regression

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 463 or equivalent
Description:
One-way and two-way analysis of variance; multiple comparisons involving means; fixed and random effects; simple and multiple linear regression; analysis of covariance; interactions; correlation and partial correlation; multicollinearity; model selection; model checking.
Offered:
Fall

BST 465 Design of Clinical Trials

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 463 or equivalent
Description:
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.
Offered:
Spring

BST 466 Categorical Data Analysis

Restrictions:
Permission of instructor required for undergraduates
Prerequisites:
BST 464 or equivalent
Description:
Measures of association for categorical outcomes; contingency table analysis; regression analysis for binary, polytomous, count and time-to-event responses; emphasis on general ideas and applications of models and methods using statistical software such as SAS; review of necessary theory underlying likelihood and nonparametric inference as it pertains to the development of relevant models and test statistics.
Offered:
Spring

BST 467 Applied Statistics in the Biomedical Sciences

Restrictions:

Registration and completion of IND 418 Biostatistics Bootcamp required for all registrants.
Description:
Experimental design, statistical inference, hypothesis testing, linear and non-linear regression analysis and model diagnosis, analysis of time-to-event data, sample size and power calculation, and genomic data analysis.

Offered:
Spring

BST 476 Introduction to Linear Models

Prerequisites:
STT 203 or STT 212 or BST 463
Description:
Simple and multiple regression models; least-squares estimation; hypothesis testing; interval estimation; prediction; matrix formulation of the general linear model; polynomial regression; analysis of variance; analysis of covariance; methods for simultaneous inference; residual analysis and checks of model adequacy.
Offered:
Fall

BST 479 Generalized Linear Models

Prerequisites:
BST 411 and BST 426
Description:
Generalized linear models; computational techniques for model fitting; logistic and conditional logistic regression; Poisson and negative binominal regression; log-linear models; models for nominal and ordinal categorical data; quasi-likelihood functions; model checking; nonlinear regression models.
Offered:
Fall

BST 487 Seminar in Statistical Literature

Description:
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.
1 credit
Offered:
Fall and Spring

BST 491 Reading Course at the Master's Level

Description:
Special work, arranged individually.
Credit varies

BST 493 Internship/Applied Project

Description:
As required for completion of the M.S. degree in medical statistics, the student works on a medical research project under the guidance of department faculty or under supervision in an industrial setting. 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.
Offered:
Fall / Spring / Summer

Description:
Credit varies

BST 496 Wet/Dry Lab Rotation

Description:

Students will choose a bioinformatics experimental or computational lab for rotations.

BST 511 Topics in Statistical Inference I

Prerequisites:
Varies by topic
Description:
Advanced topics in statistical inference and/or decision theory. Recent topics include:
Offered:
Fall

BST 512 Topics in Statistical Inference II

Prerequisites:
Varies by topic
Description:
Advanced topics in statistical inference and/or decision theory. Recent topics include:
Offered:
Spring

BST 513 Analysis of Longitudinal and Dependent Data

Prerequisites:
BST 401 and BST 411 and BST 426
Description:
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.
Offered:
Spring

BST 514 Survival Analysis

Prerequisites:
BST 411, BST 411, and BST 412
Description:
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.
Offered:
Fall

BST 531 Nonparametric Inference

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

Prerequisites:
BST 412
Description:
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
Description:
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

Restrictions:
Permission of instructor required
Description:
Advanced statistical methods for data analysis.

BST 570 Topics in Biostatistics

Restrictions:
Permission of instructor required
Description:

BST 582 Introduction to Statistical Consulting

Restrictions:Permission of instructor required

Description:Formal instruction on developing and managing consulting relationships.

BST 590 Supervised Teaching

Description:
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

Description:
Special work for doctoral candidates, arranged individually.

BST 592 Supervised Statistical Consulting

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

Description:
Credit varies

Smoothing Methods (BST 511 and BST 512)

Prerequisites:
BST 412, BST 426, and BST 478
Description:
The course will cover 3 main topics: (1) density estimation methods, including histograms, frequency polygons, kernel density estimators, local likelihood density estimators, and penalized likelihood and spline-based estimators; (2) nonparametric regression, with an emphasis on local polynomial modeling and some discussion of cubic smoothing splines; and (3) generalized additive models (GAM). Attention will be paid to locally varying smoothing parameters, boundary effects and corrections, consistency, rates of convergence, extensions to higher dimensions and the "curse of dimensionality," limit distributions, smoothing parameter selection, and kernel choice, including higher-order and equivalent kernels.

Frailty Models in Survival Analysis (BST 511 and BST 512)

Prerequisites:
BST 411, BST 479 or BST 514
Description:
The notion of frailty provides a convenient way to introduce random effects, association and unobserved heterogeneity into models for survival data. In its simplest form a frailty is an unobserved random proportionality factor which modifies the hazard function of an individual, or of related individuals. In essence the concept goes back to the 1920 work of Greenwood and Yule on "accident proneness." The term frailty itself was introduced by Vaupel, Manton and Stallard in 1979 and applications in survival analysis were popularized in a series of papers by P. Hougaard. Applications to multivariate survival data date from a seminal 1978 paper by D. Clayton.

The course will provide a general introduction to frailty models for univariate and multivariate survival data and for repeated events. We will discuss the formulation of frailty models and identifiability aspects, connections with two-sample rank tests for survival data and with measures of association for bivariate survival data. Parametric and semiparametric methods of fitting frailty models will be reviewed, including the use of the EM algorithm and related approaches including hierarchical likelihood. Extensions to correlated frailty models will also be described. The distinction between "conditional models," in which the effect of observed covariates is described conditionally on the value of the random effect and "marginal models," which integrate over the unobserved effect, will be emphasized.

Classical survival analysis methodology, including the Kaplan-Meier estimator and Cox's regression model, will be reviewed briefly at the start of the course.

The Bootstrap, The Jackknife, and Resampling Methods (BST 511 and BST 512)

Prerequisites:
BST 412 and BST 478

Description:
Bootstrap methods, including the nonparametric, parametric, smoothed, m-out-of-n, and randomly weighted bootstrap procedures, will be presented for estimating standard errors, constructing various types of confidence intervals, performing hypothesis tests, and estimating bias. One- and two-sample problems, regression, correlated data, and more general inference problems will be considered. Jackknife and cross-validation methods will also be discussed.