BST 601. Biostatistics. This course will introduce students to the Logic and language of scientific methods in life science research. Students will learn to use basic statistics in testing hypotheses and setting confidence limits. Simple and multiple regression and elementary experimental designs will be introduced as well. 4 hours. Fall/Spring.
BST 603. Introductory Biostatistics for Graduate Biomedical Sciences. This course will provide non-biostatistics students seeking a Graduate Biomedical Sciences (GBS) degree with the ability to udnerstand introductory biostatistics concepts. 3 hours. As needed.
BST 611. Intermediate Statistical Analysis I. - Students will gain a thorough understanding of basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and non-parametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant analysis using current software such as the Statistical Analysis System (SAS). 3 hours. Fall/Spring.
BST 612. Intermediate Statistical Analysis II. - This course will introduce students to the basic principle of tools of simple and multiple regression. A major goal is to establish a firm foundation in the discipline upon which the applications of statistical and epidemiologic inference will be built. Prerequisite: BST 611 or Permission of Instructor. 3 hours. Spring/Summer.
BST 613. Intermediate Statistical Analysis III. - Continuation of concepts in BST 611/612, intended to introduce students to additional general concepts in biostatistics beyond an introductory level. The course will include a broad overview of three areas: 1) categorical, ordinal, and count methods with proportional odds model and Poisson regression; 2) survival analysis and event outcome data with Kaplan-Meier, proportional hazards, and repeated events; 3) repeated measures, mixed models, hierarchical modeling for longitutinal and missing data. Study design, analysis, interpretation of results, power an sample size estimation, and non-parametric alternatives will be presented for all topic areas. Prerequisite: BST 612. 3 hours. Fall.
BST 619. Data Collection and Management. - Basic concepts of study design, forms design, quality control, data entry, data management and data analysis. Hands-on experience with data entry systems, e.g., DBASE, and data analysis software, e.g., PC-SAS. Exposure to other software packages as time permits. Prerequisites: BST 611; Previous computer experience or workshop on microcomputers highly recommended. 3 hours. Spring (even years).
BST 620. Applied Matrix Analysis. - Vector and matrix definitions and fundamental concepts; matrix factorization and application. Eigenvalues and eigenvectors, functions of matrices, singular and ill-conditioned problems. Prerequisite: BST 622. 3 hours. As needed.
BST 621. Statistical Methods I. - Mathematically rigorous coverage of applications of statistical techniques designed for biostatistics majors and others with sufficient mathematical background. Statistical models and applications of probability; commonly used sampling distributions; parametric and nonparametric one and two sample tests and confidence intervals; analysis of contingency tables; simple linear regression and analysis of variance. Prerequisites: A year of calculus and linear algebra. 3 hours. Fall.
BST 622. Statistical Methods II. - Continuation of concepts in BST 621, extended to multiple linear regression; analysis of variance, analysis of covariance, multiple analysis of variance; use of contrasts and multiple comparisons procedures; simple and multiple logistic regression, and an introduction to survival analysis. Prerequisites: BST 621. 3 hours. Spring.
BST 623. General Linear Models. - Simple and multiple regression using matrix approach; weighted and nonlinear regression; variable selection methods; modeling techniques; regression diagnostics and model validation; systems of linear equations; factorial designs; blocking; an introduction to repeated measures designs; coding schemes. Prerequisite: BST 622. 3 hours. Fall.
BST 624. Experimental Designs. - Intermediate experimental design and analysis of variance models using matrix approach. Factorial and nested (hierarchical) designs; blocking; repeated measures designs; Latin squares; incomplete block designs; fractional factorials; confounding. Prerequisites: Matrix algebra and BST 623. 3 hours. As needed.
BST 625. Design and Conduct of Clinical Trials. - Concepts of clinical trials; purpose, design, implementation and evaluation. Examples and controversies presented. Prerequisite: BST 611 and 612 or permission of instructor. Pass/No Pass. 3 hours. Summer.
BST 626/626L. Data Management/Reporting with SAS. - A hands-on exposure to data management and report generation with one of the most popular statistical software packages. Concurrent registration in BST 626 and BST 626L is required. 3 hours. Fall.
BST 631. Statistical Theory I. - Fundamentals of probability; conditional probability and independence; distribution, density, and mass functions; random variables; moments and moment generating functions; discrete and continuous distributions; exponential families, joint, marginal, and conditional distributions; transformation and change of variables; convergence concepts; sampling distributions; order statistics; random number generation. Prerequisite: Advanced calculus. 4 hours. Fall.
BST 632. Statistical Theory II. - Point interval estimation; sufficiency and completeness; ancillary statistics; maximum likelihood and moment estimators; best unbiased estimator; hypothesis and significance testing; likelihood ratio tests and uniformly most powerful tests; confidence interval estimation; asymptotic properties of estimators and tests; introduction to Bayesian inference. Prerequisite: BST 631. 4 hours. Spring.
BST 640. Nonparametric Methods. - Properties of statistical tests; order statistics and theory of extremes; median tests; goodness of fit; tests based on ranks; location and scale parameter estimation; confidence intervals; association analysis; power and efficiency. Prerequisite: BST 622, BST 632. 3 hours. As needed.
BST 655. Categorical Data Analysis. – Intermediate level course with emphasis on understanding the discrete probability distributions and the correct application of methods to analyze data generated by discrete probability distributions. The course covers contingency tables, Mantel-Haenszel tests, measures of association and of agreement, logistic regression models, regression diagnostics, proportional odds, ordinal and polytomous logistic regression, Poisson regression, log linear models, analysis of matched pairs and repeated categorical data. Prerequisite: BST 622 or equivalent recommended. 3 hours. Fall.
BST 660. Applied Multivariate Analysis. - Analysis and interpretation of multivariate general linear models including multivariate regression, multivariate analysis of variance/covariance, discriminant analysis, multivariate analysis of repeated measures, canonical correlation, and longitudinal data analysis for general and generalized linear models. Extensive use of SAS, SPSS, and other statistical software. Prerequisite: BST 623. 3 hours. As needed.
BST 661. Structural Equation Modeling. - Basic principles of measurements; factor analysis and latent variable models; multivariate predictive models including mediation mechanisms and moderator effects; path analysis; integrative multivariate covariance models, methods of longitudinal analysis. Prerequisite: BST 623. 3 hours. As needed.
BST 665. Survival Analysis. - Kaplan-Meier estimation; Parametric survival models; Cox proportional hazards regression models; sample size calculation for survival models; competing risks models; multiple events models. Prerequisite: BST 622. 3 hours. Spring.
BST 670. Sampling Methods. - Simple random, stratified, cluster, ratio regression and systematic sampling; sampling with equal or unequal probabilities of selection; optimization; properties of estimators; non-sampling errors; sampling schemes used in population research; methods of implementation and analyses associated with various schemes. Prerequisite: BST 631. 3 hours. As needed.
BST 675. Introduction to Statistical Genetics. – This class will introduce students to population genetics, genetic epidemiology, microarray and proteomics analysis, Mendelian laws, inheritance, heritability, test cross linkage analysis, QTL analysis, human linkage and human association methods for discrete and quantitative traits. Prerequisite: BST 611 or BST 621. 3 hours. Spring (odd years).
BST 676. Genomic Data Analysis. - The purpose of this class will be to teach graduate students statistics methods that underlie the analysis of data generated by high throughput genomic technologies, as well as issues in the experimental design and implementation of these technologies. High throughput technologies that will be covered include microarrays, proteomics, and second generation sequencing. Prerequisites: BST 611 or 621. BST 675 recommended. 3 hours. Spring (even years).
BST 680. Statistical Computing with R. This course is mainly focused on R and how to use R to conduct basic statistical computing. The course contains three themes: R programming, intruduction to high performance computing, and basics of statistical computing. Prerequisites: BST 621, BST 622, and BST 626, (Introductory Probability and Inference) or equivalent. 2 hours. (Every other year).
BST 691. Biostatistics Pre-doctoral Seminar Series. This course provides an opportunity for students to learn about ongoing research in the field of biostatistics, clinical trials, and statistical genetics. Pass/No Pass. 1 hour. Fall/Spring.
BST 695. Special Topics. - This course is designed to cover special topics in Biostatistics that are not covered in regular 600 level courses, but suited for Masters students in Biostatistics and doctoral students in other related disciplines. 1-3 hours.
BST 697. Internship in Biostatistics. - Pass/No Pass. 1-6 hours.
BST 698. Non-Thesis Research. - Pass/No Pass. 1-6 hours.
BST 699. Master's Thesis Research. - Prerequisite: Admission to candidacy for MS degree. Pass/No Pass. 1-12 hours.
BST 723. Theory of Linear Models. - Multivariate normal distributions and quadratic forms; least square estimation; nested models; weighted least squares, testing contrasts; multiple comparisons; polynomial regression; maximum likelihood theory of log linear models. Prerequisite: BST 632. 3 hours. Fall (odd years).
BST 725. Advanced Clinical Trials I. - This course will provide students with a basic understnding of the fundamental statistical principles involved in the design and conduct of clinical trials. Important topics of discussion will include data management, quality assurance, endpoints, power analysis, interim analysis, adaptive designs, and genetic issues in clinical trials. Prerequisites: BST 611, 612, and 625. 3 hours. Spring.
BST 726. Advanced Clinical Trials II. - This course builds on the knowledge gained in BST 725 in order to develop a more thorough understanding of the basic methodology behind power analysis, interim data monitoring, analysis of missing data, and adaptive designs. The class involves discussions of recent publications dealing with current topics of interest in clinical trials. Each student must conduct, summarize, and present a course project based on a more in-depth exploration of one of the topics introduced in the BST 725 course. Prerequisites: BST 621, 622, 625, 631, 632 and 725. 3 hours. Summer.
BST 735. Advanced Inference. - Stochastic convergence and fundamental inequalities; weak convergence and the central limit theorems; large sample behavior of the empirical distribution and other statistics; Asymptotic behavior of estimators and tests with particular attention to LR, score and Walt tests. Prerequisites: BST 631 and 632. 4 hours. Spring (odd years).
BST 740. Bayesian Analysis. - To introduce the student to the basic principles and tools of Bayesian Statistics and most importantly to Bayesian data analysis techniques. A major goal is to establish a firm foundation in the discipline upon which the applications of statistical and epidemiologic inference will be built. The practical part of the course will be based on Bugs (either WinBugs or OpenBugs), possibly accessed through R with the existing tools for the interface (R packages: R2WinBugs or RBugs, coda). This will enable participants to take the practical examples all the way to the reporting stage in terms of tabulations and graphics. Prerequisites: BST 632. 3 hours. Fall (even years).
BST 741. Advanced Bayesian Analysis II. To illustrate advanced approaches to Bayesian modeling and computation in statistics. We begin with a brief description of the basic principle and concepts of Bayesian statistics. We then study advanced tools in Bayesian modeling and computation. A variety of models are covered, including multilevel/hierarchical linear and generalized linear models, models for robust inference, mixture models, multivariate models, nonlinear models, missing data, and Bayesian model selection. We also introduce some applied areas of modern Bayesian methods, such as genetics/genomics and clinical trials. The practical part of the course will be based on Bugs (eigher WinBugs or OpenBugs), possibly accessed through R with the exisiting tools for the interface (R packages: R2WinBUGS or BRugs, coda). This will enable particiapnts to take the practical examples all the way to the reporting stage in terms of tabulations, graphics etc. Prerequisites: BST 631 and 632. BST 740 would be helpful but not absolutely required. 3 hours. Fall (odd years).
BST 750. Stochastic Modeling. - Poisson processes; random walks; simple diffusion and branching processes; recurrent events; Markov chains in discrete and continuous time; birth and death process; queuing systems; applications to survival and other biomedical models. Prerequisite: BST 632. 3 hours. As needed.
BST 760. Generalized Linear and Mixed Models. - Generalized linear models; mixed models; and generalized estimating equations. Prerequisite: BST 723. 3 hours. Spring (even years).
BST 765. Advanced Computational Methods. - Numerical algorithms useful in biostatistics including likelihood maximization using the Newton-Raphson method, EM algorithm, numerical integration using quadratic and Monte-Carlo methods, interpolation using splines, random variate generation methods, data augmentation algorithm, and MCMC and Metropolis-Hastings algorithm; randomization tests; resampling plans including bootstrap and jackknife. Prerequisites: BST 632. 3 hours. Fall (even years).
BST 775. Statistical Methods for
BST 776. Statistical Methods for Genetic Analysis II. - This course builds on the knowledge gained in BST 775 with rigorous mathematical and statistical treatment of methods for localizing genes and environmental effects involved in the etiology of complex traits using case-control and pedigree data. Prerequisites: BST 775; Knowledge of SAS and programming languages such as C++, and basic knowledge of multivariate methods and Markov chain theory is highly recommended. 3 hours. Spring (even years).
BST 793. Bioistatistics Post-doctoral Seminar Series. This course provides an opportunity for post-doctoral students to learn about ongoing research in the field of biostatistics, clinical trials, and statistical genetics. Reserved for BST Postdoctoral students. Pass/No Pass. 3 hours. Fall/Spring.
BST 795. Advanced Special Topics. - This course is designed to cover advanced special topics in Biostatistics that are not covered in regular 700 level courses, but suited for doctoral students in Biostatistics. Prerequisites: BST 622 and 632. Pass/No Pass. 1-3 hours.
BST 798. Non-Dissertation Research. - Pass/No Pass. 1- 6 hours.
BST 799. Doctoral Dissertation Research. - Prerequisite: Admission to candidacy for PhD. Pass/No Pass. 1 -12 hours.