Fall Term
MTH 624 – Introduction to Probability Theory (3 cr)
Probability spaces, random variables, expectation, limit theorems.
MTH 642 – Statistical Analysis (3 cr)
Statistical inference about one or two populations from interval, ordinal and categorical data; analysis of variance; simple and multiple linear regression; designing research studies.
EPH 600 – Introduction to the Science and Practice of Public Health (3 cr)
This introductory course will provide students with the opportunity to explore and analyse contemporary public health issues and provide a history and a context that will allow students to better understand the field of public health, its core disciplines and their role as future public health professionals.
Requisite: Must be in either Program Plan, BSTS, EPID, EPID1, PREV, MDRP.
EPH 621 – Fundamentals of Epidemiology (3 cr)
Principles and methods of epidemiology. Descriptive epidemiology, environmental and other risk factors, detection of outbreaks, basic demography, and etiologic studies.
Requisite: Must be in either Program Plan, BSTS, EPID, EPID1, PREV, MDRP.
BST 640 – Modern Numerical Multivariate Methods (3 cr)
This course covers multivariate topics from both a classical as well as modern perspective. Topics to include: Multivariate Normal Distribution; Spectral Decomposition; Principal Component Analysis; Canonical Correlation Analysis; Newton's Method; Steepest Descent; Gradient Boosting; Coordinate Descent Algorithms; Trees; Forests; Discriminant Analysis. The R programming language (http://www.r-project.org) will be used extensively throughout the course for computation and statistical analysis.
BST 650 – Topics in Biostatistical Research (4 cr)
The course consists of a series of research level presentations in contemporary biostatistics research (broadly defined) by diverse outside speakers as well as faculty in the Division of Statistics or in other units on campus who are hosting presentations in biostatistics research. The emphasis will be on new methodologies and new developments in existing methodologies. However, recent developments on the implementation and comparison of methodology and on data types may also be included.
BST 665 – Design and Analysis of Clinical Trials (3 cr)
This first part of this course is an advanced treatment of the key ideas undergirding the design and analysis of contemporary clinical trials. By the end of the course, students will have learned the statistical foundations of Phase I, II, and III trials from the standpoint of classical Frequentist, Bayesian, and adaptive designs. In addition, students will learn the usual mechanisms for preparing a clinical trial protocol, data safety and monitoring, interim analysis, and proper close out of a trial.
BST 690 – Theory of Survival Analysis (3 cr)
Survival analysis is an important tool of statistic with many applications. In this course, without losing sight of such applications, we will give special emphasis to the probabilistic foundations, in terms of counting processes and martingales. Topics include: Failure time models, inference in parametric models, Cox models, counting processes and martingales, likelihood, competing risks and analysis of recurrent event data. The R programming language will be used.
Prerequisite: MTH 524, MTH 525, and BST 680.
BST 830 – Doctoral Dissertation (pre-candidacy)
Required of all candidates for the PhD. The student will enroll for credit as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 730 may be taken in regular semester, nor more than six in a summer session.
BST 840 – Doctoral Dissertation (post-candidacy)
Required of all candidates for the PhD. The student will enroll for credits as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 740 may be taken in regular semester, nor more than six in a summer session.
Spring Term
MTH 625 – Introduction to Mathematical Statistics (3 cr)
Probability distributions, theory of sampling and hypothesis testing.
Prerequisite: MTH 624.
EPH 600 – Introduction to the Science and Practice of Public Health (3 cr)
This introductory course will provide students with the opportunity to explore and analyse contemporary public health issues and provide a history and a context that will allow students to better understand the field of public health, its core disciplines and their role as future public health professionals.
Requisite: Must be in either Program Plan, BSTS, EPID, EPID1, PREV, MDRP.
EPH 621 – Fundamentals of Epidemiology (3 cr)
Principles and methods of epidemiology. Descriptive epidemiology, environmental and other risk factors, detection of outbreaks, basic demography, and etiologic studies.
Requisite: Must be in either Program Plan, BSTS, EPID, EPID1, PREV, MDRP.
BST 630 – Longitudinal and Multilevel Data (3 cr)
This course offers students an introduction to linear and generalized linear models for the analysis of multi-level and longitudinal biomedical data. The course will also provide students with the opportunity to develop the skills necessary to perform analysis of these types of data using statistical software packages.
BST 650 – Topics in Biostatistical Research (4 cr)
The course consists of a series of research level presentations in contemporary biostatistics research (broadly defined) by diverse outside speakers as well as faculty in the Division of Statistics or in other units on campus who are hosting presentations in biostatistics research. The emphasis will be on new methodologies and new developments in existing methodologies. However, recent developments on the implementation and comparison of methodology and on data types may also be included.
BST 676 – Introduction to Generalized Linear Models (3 cr)
This course provides a unifying framework for formulation, estimation and inference using generalized linear models and towards the end examines some modern day extensions. Throughout the course, real data applications from medicine will be used and extensive use will be made of the R programming language.
BST 680 – Advanced Statistical Theory (3 cr)
The first part of this course is a searching treatment of many of the key ideas undergirding hypothesis testing and estimation. In particular, several of the main theorems in mathematical statistics will be stated and proved in full detail. By the end of the course, students will have acquired enough background material for the treatment of a special topic, through a mix of lectures and assignments. Topics will include asymptotic expansions, information theory and non-parametrics.
BST 691 – High Dimensional and Complex Data (3 cr)
This course will cover some salient topics in high dimensional data analysis focusing on the uniqueness of the problem and discussing various analyses including error rate control methods, model based shrinkage, prediction, set analysis, cluster analysis, bump hunting and (if time permits), graphical models.
BST 830 – Doctoral Dissertation (pre-candidacy)
Required of all candidates for the PhD. The student will enroll for credit as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 730 may be taken in regular semester, nor more than six in a summer session.
BST 840 – Doctoral Dissertation (post-candidacy)
Required of all candidates for the PhD. The student will enroll for credits as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 740 may be taken in regular semester, nor more than six in a summer session.
Summer Term
BST 610 – Introduction to Statistical Collaboration (3 cr)
This course gives students exposure to issues arising in biostatistics consulting and collaboration. Students will learn how to identify the scientific objectives of a study and to develop a statistical strategy appropriate for those objectives. The student will become familiar with problems arising in consulting situations, specifically relating to identification of study objectives and framing of research questions, study design, power and sample size determination and choice of analytical approach. The student will learn to communicate through presentation of oral and written reports, and through student and faculty critiques of these reports. This course is open only to MS and PhD Biostatistics students or instructor’s permission.
Prerequisite: Academic Plan: BSTS_PHD or BSTS_MS.
BST 830 – Doctoral Dissertation (pre-candidacy)
Required of all candidates for the PhD. The student will enroll for credit as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 730 may be taken in regular semester, nor more than six in a summer session.
BST 840 – Doctoral Dissertation (post-candidacy)
Required of all candidates for the PhD. The student will enroll for credits as determined by his/her advisor but not for less than a total of 24. Not more than 12 hours of BST 740 may be taken in regular semester, nor more than six in a summer session.
