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

BST 567 Statistical Consulting 3.0 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: Dornsife 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: Dornsife 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: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: B]

BST 570 Generalized Linear Models 4.0 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: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 569 [Min Grade: B]

BST 571 Introduction to Biostatistics 3.0 Credits

Introduction to Biostatistics provides students with an understanding of the methods of biostatistics, applicable to epidemiological and clinical studies. It emphasizes concepts and application of statistical and epidemiological thinking. Basic statistical theory, parametric statistics, correlation, regression, ANOVA, non-parametric statistics, and methods in discrete statistical analysis, along with other quantitative methods including screening tests, will be introduced. This course will emphasize hands-on experience in statistical analysis and interpretation of data from epidemiological studies.

College/Department: Dornsife School of Public Health
Repeat Status: Can be repeated 1 times for 6 credits

BST 604 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: Dornsife 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 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: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 560 [Min Grade: B]

BST 622 Advanced Clinical Trials 3.0 Credits

This course builds on the knowledge gained in BST 561, in order to develop a more thorough understanding of the basic methodology behind important statistical concepts used in the design and analysis of large, randomized clinical trials. The class will involve discussions of publications dealing with current topics of interest in clinical trials. Each student will also be asked to conduct, summarize, and present a course project based on a more in-depth exploration of one of the topics BST 561.

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 651 [Min Grade: C] and BST 561 [Min Grade: C]

BST 623 Theory of Generalized Linear and Mixed Models 3.0 Credits

This course is an advanced doctoral course, intended to familiarize students with both the theory and applications of the generalized linear and mixed models. The first third of the course will be devoted to the study of the generalized linear model. The remainder of the course will be devoted to the study of models for correlated data; in particular, we will discuss the theory and applications of the linear mixed model. If time permits, generalized estimating equations (GEEs) and generalized linear mixed models (GLMM) will be introduced.

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 651 [Min Grade: C] and BST 570 [Min Grade: C]

BST 624 Advanced Bayesian Analysis 3.0 Credits

The course provides an overview of the underlying theory of and some advanced applications of Bayesian statistical inference. In particular, the course will cover topics such as Bayesian model selection while emphasizing modern computing techniques. The course will use the freely available statistical software package, R (http://cran.r-project.org/).

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 551 [Min Grade: C] and BST 651 [Min Grade: C] and BST 604 [Min Grade: C] and BST 701 [Min Grade: C]

BST 625 Probability Models and Stochastic Processes 3.0 Credits

This course introduces basic concepts of stochastic processes. The focus of the course is on the principal stochastic or random processes most commonly used in applications as mathematical models of random phenomena that evolve over time. Topics tentatively selected include: Review of conditional probability, conditional expectation, and generating functions; Markov chains in discrete time; Poisson processes; renewal processes; Markov chains in continuous time; Brownian motion and Gaussian processes. These types of processes are fundamental to modeling time-dependent random phenomena in many areas of medical and health sciences. The emphasis will be on developing a sound understanding of the material, and many of the examples of the methods will be in the area of public health and bioinformatics.

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

BST 626 Research Skills in Biostatistics I 1.0 Credit

This course introduces doctoral students in Biostatistics to research skills necessary for writing and defending a dissertation, and more generally, for a career in research. The format and topics will vary from week-to-week, as described below. Students will be given assignments to reinforce skills presented in the class.

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

BST 627 Research Skills in Biostatistics II 1.0 Credit

This course introduces doctoral students in Biostatistics to research skills necessary for writing and defending a dissertation, and more generally, for a career in research. The format and topics will vary from week-to-week. Students will be given assignments to reinforce skills presented in the class.

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 626 [Min Grade: CR]

BST 628 Research Skills in Biostatistics III 1.0 Credit

This course introduces doctoral students in Biostatistics to research skills necessary for writing and defending a dissertation, and more generally, for a career in research. The format and topics will vary from week-to-week. Students will be given assignments to reinforce skills presented in the class. The class will meet weekly and is the third course in a three course series.

College/Department: Dornsife School of Public Health
Repeat Status: Not repeatable for credit
Prerequisites: BST 627 [Min Grade: CR]

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

BST 699 Data Analysis Project 1.0-9.0 Credit

Provides the student with experience completing a substantive data analysis in either an academic or applied setting. The project will be performed over a full term under the supervision of the advisor. Projects based in settings outside the Department are jointly-supervised by the advisor and a doctorally prepared host organization researcher.

College/Department: Dornsife School of Public Health
Repeat Status: Can be repeated multiple times for credit

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

BST 999 Biostatistics Thesis Research 1.0-9.0 Credit

Directed guidance of dissertation research, preparation for presenting dissertation research to colleagues at the dissertation seminar and preparation for the final defense.

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

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