Biostatistics

Courses

BST 551 Statistical Inference I 3.0 Credits

This course introduces probability and biostatistics theory. Topics includes basic concepts of probability, distributions, exponential families, conditional distributions and independence, expectations and transformations, moment-generating functions, probability inequalities and identities, limit theorems, and convergence concepts.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Corequisite: BST 569

BST 553 Longitudinal Data Analysis 3.0 Credits

Course covers modern statistical techniques for longitudinal data from an applied perspective. Suitable for doctoral and master students in biostatistics and doctoral students in epidemiology, clinical trials and social science analyzing longitudinal data.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: B]

BST 555 Introduction to Statistical Computing 3.0 Credits

Research projects often involve the management and manipulation of complicated sets of data. This course is designed to introduce the student to practical issues in the management and analysis of health and pharmaceutical data using the SAS programming language. Data from a variety of public health and biomedical applications will be used throughout the course to illustrate the principles of data management and analysis for addressing biomedical and health-related hypotheses.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit

BST 557 Survival Data Analysis 3.0 Credits

This course covers the basic techniques of survival analysis. These approaches are useful in analyzing cohort data, which are common in health studies, when the main interest outcome is the onset of event and time to event is known. The response is often referred to as failure time, survival time, or event time, and this course will introduce students to methods necessary for analyzing this type of data.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: C]

BST 558 Applied Multivariate Analysis 3.0 Credits

This course introduces students to statistical methods for describing and analyzing multivariate data. Topics to be covered include basic matrix algebra, multivariate normal distribution; linear models with multivariate response, multivariate analysis of variance; profile analysis, dimension reduction techniques, including principle component analysis, factor analysis, canonical correlation, multidimensional scaling; discriminate/cluster analysis; and classification/regression trees.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 551 [Min Grade: C]

BST 559 Intermediate SAS 3.0 Credits

This course is designed to teach students the art of data management. The focus of the course is the application of prior coursework, specifically methodological courses in epidemiology and biostatistics, to issues in data management and analysis. Issues in data management are typically specific to study design and analysis and, as such, methods to handle data will focus on the many ways variables may be operationalized to answer research questions. The course will cover a number of topics and aims to provide a language of data that will allow the students who complete the course to tackle any methodological data issue they may encounter in the future.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 555 [Min Grade: B] or EPI 501 [Min Grade: B]

BST 560 Intermediate Biostatistics I 3.0 Credits

This course focuses on an overview of the linear modeling methods most commonly used in epidemiological and public health studies. Models include simple/multivariate linear regression, analysis of variance, logistic/conditional regression, Poisson regression and models for survival data. Focus is on implementing models and interpreting results.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: EPI 501 [Min Grade: B]

BST 561 Design & Analysis of Clinical Trials 3.0 Credits

In this course, we will introduce the process of performing a clinical trial, including introducing the different phases of study, the approaches to data management for trials, interim analyses and adaptive clinical trials, sample size calculations for clinical trials, and issues of safety in trials. Students will have the opportunity to learn the process of designing, implementing, running and analyzing a clinical trial using real examples.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: EPI 501 [Min Grade: B]

BST 565 Applied Bayesian Analysis 3.0 Credits

The course provides a practical introduction to Bayesian statistical inference, which is now at the core of many advanced methods. The course will compare traditional frequentist estimation, which relies on maximization methods, to Bayesian estimation of the posterior distribution. Students will learn numerical integration methods, such as Markov Chain Monte Carlo, to obtain these various distributions and ultimately make inference in a Bayesian framework. The course will also use the freely available statistical software, R (http://cran.r-project.org/).

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 553 [Min Grade: C] and BST 651 [Min Grade: C] and BST 701 [Min Grade: C]

BST 567 Statistical Consulting 2.0 Credits

The objective of this course is to introduce biostatistics graduate students to the fundamental aspects of statistical consulting and to provide training for being an effective statistical consultant. Topics tentatively selected include: Roles and responsibilities of biostatisticians in collaboration with scientists and other clients, oral and written communication skills, sample size and power calculations, study design, how to help researchers formulate their scientific questions in quantifiable terms, how to deal with missing data, and how to write statistical analysis.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: EPI 501 [Min Grade: B] or BST 555 [Min Grade: C]

BST 568 Nonparametric and Semiparametric Models 3.0 Credits

The objective of this course is to introduce students to the fundamental concepts and applicable techniques of non-parametric and semi-parametric models, in particular, nonlinear functional relationships in regression analyses. Topics tentatively selected include: Density estimation, smoothing, non-parametric regression, additive models, semi-parametric mixed models, and generalized additive models.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 701 [Min Grade: B-]

BST 569 Linear Statistical Models 4.0 Credits

The objective of this course is to introduce students to linear regression models (computation, theoretical properties, model interpretation and application). Topics include: Review of basic concepts of matrix algebra that are particularly useful in linear regression, and basic R programing features; (weighted) least square estimation, inference and testing; regression diagnostics, outlier influence; and variable selection and robust regression.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: B]

BST 570 Generalized Linear Models 3.0 Credits

The objective of this course is to introduce students to generalized linear regression models (theoretical properties, model interpretation and application). Topics include: 1) Review of categorical data and related sampling distributions; 2) Two/Three-way contingency tables; 3) logistic regression and poission regression; 4) loglinear models for contingency tables ; 5) generalized linear mixed models for categorical responses; 6) principles of MLE in generalized linear model.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 569 [Min Grade: B]

BST 620 Intermediate Biostatistics II 3.0 Credits

The course builds on material from Intermediate Biostatistics I, introducing additional core biostatistical methods such as Poisson and negative binomial regression, random and mixed effects models, survival analysis techniques, and nonparametric methods. We will focus on exploratory data analysis, model building, model checking, and diagnostics, developing a flexible, critical approach to statistical data analysis.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: B]

BST 651 Statistical Inference II 4.0 Credits

This course is a continuation of Biostatistics Theory I. The objective of this course is to introduce students to the fundamental concepts and methods of statistical inference. Topics include: point and interval estimation, methods of moments, maximum likelihood estimation, Bayes estimates, hypothesis testing, Neyman-Pearson lemma, likelihood ratio tests and large sample approximation; Bayesian analysis.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 551 [Min Grade: C]

BST 701 Advanced Statistical Computing 3.0 Credits

This course expands on computational methods used in biostatistics. It covers numerical techniques, programming, and simulations and will connect these to fundamental concepts in probability and statistics. The course will use the statistical software, R, to apply these concepts and enable the practical application of biostatistical models to real-world problems.

College/Department: School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 551 [Min Grade: B]

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