Curriculum - Class of 2022

Curriculum Overview

Please note the curriculum below pertains to the 2022 graduating class and following years. The Curriculum has been revised as of August 2020. The Master of Biostatistics degree, a professional degree awarded by the Duke University School of Medicine, requires 50 total credit hours (44 credit hours of graded coursework and 6 credit hours of Master’s Project).  In August 2020, all elective courses were assigned 3 credit hours.   

Year 1

All first year students will take the same courses unless they place out due to prior experience. No tracks are selected at this point.

 

First Year: 26 graded coursework credit hours

 

YEAR

FALL

SPRING

SUMMER

YEAR ONE

BIOSTAT 701 (3)
 

BIOSTAT 702 (3)

 

BIOSTAT 703 (3)

 

BIOSTAT 703L (NC)

 

BIOSTAT 721 (3)

 

BIOSTAT 801 (1)

BIOSTAT 704 (3)

 

BIOSTAT 705 (3)

 

BIOSTAT 706 (3)

 

BIOSTAT 722 or 821* (3)

 

BIOSTAT 802 (1)
 

 

*Permission required to take 821 rather than 722.

 

 

 

Qualifying Examination
Covers BIOSTAT 701-706 content.
First offering is mid-June.
Second offering is mid-August.

 

Practicum
Experiential learning opportunity with real data may be conducted at any point during the 2-year program.

 

Year 2

After consultation with the Director of Graduate Studies, all students will enroll in approved courses during fall registration. Students will officially enter a designated track after the end of drop/add in the second-year fall semester.

All Tracks - Clinical and Translational, Biomedical Data Sciences, and Mathematical Statistics

Second Year: 24 credit hours - master’s project (6) plus graded coursework credit hours (18)

YEAR

FALL

SPRING

SUMMER

YEAR TWO

BIOSTAT 720 (3) -Required

+

Choose 9 credit hours*

BIOSTAT 707 (3)

BIOSTAT 710 (3)

BIOSTAT 713 (3)

BIOSTAT 719 (3)

BIOSTAT 823 (3)

BIOSTAT 906 (3)

BIOSTAT 911 (3)

BIOSTAT 914 (3)

STA 611 (3)

STA 671D (3)

STA 711 (3)

MA 531(3)

MA 731 (3)

BIOSTAT 720 (3) -Required

+

Choose 9 credit hours*

BIOSTAT 708 (3)

BIOSTAT 709 (3)

BIOSTAT 714 (3)

BIOSTAT 718 (3)

BIOSTAT 822 (3)

BIOSTAT 824 (3)

BIOSTAT 905 (3)

STA 602L (3)

STA 663L (3)

MA 531 (3)

MA 731 (3)

 

 

Graduation

Practicum

All candidates for the Master of Biostatistics degree are required to complete a practicum. The practicum is an experiential learning opportunity. The main goal of the practicum is to allow students to develop their analytic ability, biological knowledge, and communication skills. The practicum is typically completed during the summer after the first year, but can be completed during the second year.

Qualifying Examination

All candidates for the Master of Biostatistics degree are required to pass a written Qualifying Examination demonstrating their mastery of fundamental concepts acquired through completion of the first-year core courses (BIOSTAT 701 – 706 inclusive). Students are expected to take the Qualifying Examination after completing the first year of study in the program and prior to beginning their elective coursework.

Master's Project (two semesters, totalling six credits)

All candidates for the Master of Biostatistics degree are required to complete a Master’s Project. Completed in the second year, the two-semester course demonstrates the student's mastery of core statistical concepts and the practice of biostatistics. BIOSTAT 720. Master's Project  (3 credits per semester for a total of six credits) 

Course Descriptions

BIOSTAT 701: Introduction to Statistical Theory and Methods I: This course provides a formal introduction to the basic theory and methods of probability and statistics. It covers topics in probability theory with an emphasis on those needed in statistics, including probability and sample spaces, independence, conditional probability, random variables, parametric families of distributions, and sampling distributions. Core concepts are mastered through mathematical exploration and linkage with the applied concepts studied in BIOSTAT 704. Prerequisite(s): 2 semesters of calculus or its equivalent (multivariate calculus preferred). Familiarity with linear algebras is helpful. Corequisite(s): BIOSTAT 702, BIOSTAT 703. Credits: 3

BIOSTAT 702: Applied Biostatistical Methods I: This course provides an introduction to study design, descriptive statistics, and analysis of statistical models with one or two predictor variables. Topics include principles of study design, basic study designs, descriptive statistics, sampling, contingency tables, one- and two-way analysis of variance, simple linear regression, and analysis of covariance. Both parametric and non-parametric techniques are explored. Core concepts are mastered through team-based case studies and analysis of authentic research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 703. Computational exercises will use the R and SAS packages. Prerequisite(s): 2 semesters of calculus or its equivalent (multivariate calculus preferred). Familiarity with linear algebras is helpful. Corequisites(s): BIOSTAT 701, BIOSTAT 703, BIOSTAT 721. Credits: 3

BIOSTAT 703: Introduction to the Practice of Biostatistics I: This course provides an introduction to biology at a level suitable for practicing biostatisticians and directed practice in techniques of statistical collaboration and communication. With an emphasis on the connection between biomedical content and statistical approach, this course helps unify the statistical concepts and applications learned in BIOSTAT 701 and BIOSTAT 702. In addition to didactic sessions on biomedical issues, students are introduced to different areas of biostatistical practice at Duke University Medical Center. Biomedical topics are organized around the fundamental mechanisms of disease from both evolutionary and mechanistic perspectives, illustrated using examples from infectious disease, cancer and chronic /degenerative disease. In addition, students learn how to read and interpret research and clinical trial papers. Core concepts and skills are mastered through individual reading and class discussion of selected biomedical papers, team-based case studies and practical sessions introducing the art of collaborative statistics. Corequisite(s): BIOSTAT 701, BIOSTAT 702. Credits: 3

BIOSTAT 703L: Introduction to the Practice of Biostatistics I Lab: The lab will be an extension of the course. The lab will be run like a journal club. The lab will instruct students how to dissect a research article from a statistical and scientific perspective. The lab will also give students the opportunity to present on material covered in the co-requisite course and to practice the communication skills that are a core tenant of the program. Corequisite(s): BIOSTAT 703 or permission of the Director of Graduate Studies. Credits: 0

BIOSTAT 704: Introduction to Statistical Theory and Methods II: This course provides formal introduction to the basic theory and methods of probability and statistics. It covers topics in statistical inference, including classical and Bayesian methods, and statistical models for discrete, continuous and categorical outcomes. Core concepts are mastered through mathematical exploration, simulations, and linkage with the applied concepts studied in BIOSTAT 705. Prerequisite(s): BIOSTAT 701 or its equivalent. Corequisite(s): BIOSTAT 705, BIOSTAT 706. Credits: 3

BIOSTAT 705: Applied Biostatistical Methods II: This course provides an introduction to general linear models and the concept of experimental designs. Topics include linear regression models, analysis of variance, mixed-effects models, generalized linear models (GLM) including binary, multinomial responses and log-linear models, basic models for survival analysis and regression models for censored survival data, and model assessment, validation and prediction. Core concepts are mastered through statistical methods application and analysis of practical research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 706. Computational examples and exercises will use the SAS and R packages. Prerequisite(s): BIOSTAT 702 or its equivalent. Corequisite(s): BIOSTAT 704, BIOSTAT 706, BIOSTAT 722. Credits: 3

BIOSTAT 706: Introduction to the Practice of Biostatistics II: This course revisits the topics covered in BIOSTAT 703 in the context of high-throughput, high-dimensional studies such as genomics and transcriptomics. The course will be based on reading of both the textbook and research papers. Students will learn the biology and technology underlying the generation of “big data,” and the computational and statistical challenges associated with the analysis of such data sets. As with BIOSTAT 703, there will be strong emphasis on the development of communication skills via written and oral presentations. Prerequisite(s): BIOSTAT 703. Corequisite(s): BIOSTAT 704, BIOSTAT 705. Credits: 3

BIOSTAT 707: Statistical Methods for Learning and Discovery: This course surveys a number of techniques for high dimensional data analysis useful for data mining, machine learning and genomic applications, among others. Topics include principal and independent component analysis, multidimensional scaling, tree-based classifiers, clustering techniques, support vector machines and networks, and techniques for model validation. Core concepts are mastered through the analysis and interpretation of several actual high dimensional genomics datasets. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 708: Clinical Trial Design and Analysis: Topics include: history/background and process for clinical trial, key concepts for good statistics practice (GSP)/good clinical practice (GCP), regulatory requirement for pharmaceutical/clinical development, basic considerations for clinical trials, designs for clinical trials, classification of clinical trials, power analysis for sample size calculation, statistical analysis for efficacy evaluation, statistical analysis for safety assessment, implementation of a clinical protocol, statistical analysis plan, data safety monitoring, adaptive design methods in clinical trials (general concepts, group sequential design, dose finding design, and phase I/II or phase II/III seamless design) and controversial issues in clinical trials. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 709: Observational Studies: Methods for causal inference, including confounding and selection bias in observational or quasi-experimental research designs, propensity score methodology, instrumental variables, and methods for non-compliance in randomized clinical trials. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 710: Statistical Genetics and Genetic Epidemiology: Topics from current and classical methods for assessing familiality and heritability, linkage analysis of Mendelian and complex traits, family-based and population-based association studies, genetic heterogeneity, epistasis, and gene-environmental interactions. Computational methods and applications in current research areas. The course will include a simple overview of genetic data, terminology, and essential population genetic results. Topics will include sampling designs in human genetics, gene frequency estimation, segregation analysis, linkage analysis, tests of association, and detection of errors in genetic data. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 713: Survival Analysis: Introduction to concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, regression techniques, applications to clinical trials. Interval censoring, informative censoring, competing risks, multiple events and multiple endpoints, time dependent covariates; nonparametric and semi-parametric methods. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 714: Categorical Data Analysis: Topics in categorical modeling and data analysis/contingency tables; measures of association and testing; logistic regression; log-linear models; computational methods including iterative proportional fitting; models for sparse data; Poisson regression; models for ordinal categorical data, and longitudinal analysis. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 718: Analysis of Correlated and Longitudinal Data: Topics include linear and nonlinear mixed models; generalized estimating equations; subject specific versus population average interpretation; and hierarchical model. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 719: Generalized Linear Models: The class introduces the concept of exponential family of distributions and link function, and their use in generalizing the standard linear regression to accommodate various outcome types. Theoretical framework will be presented but detailed practical analyses will be performed as well, including logistic regression and Poisson regression with extensions. Majority of the course will deal with the independent observations framework. However, there will be substantial discussion of longitudinal/clustered data where correlations within clusters are expected. To deal with such data the Generalized Estimating Equations and the Generalized Linear Mixed models will be introduced. An introduction to a Bayesian analysis approach will be presented, time permitting. Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722 or their equivalents, or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 720: Master’s Project: Completed during a student’s final year of study, the master’s project is performed under the direction of a faculty mentor and is intended to demonstrate general mastery of biostatistical practice. Prerequisite(s): BIOSTAT 701 through BIOSTAT 706. Credits: 3 in Fall Semester and 3 in Spring Semester

BIOSTAT 721: Introduction to Statistical Programming I (R): This class is an introduction to programming in R, targeted at statistics majors with minimal programming knowledge, which will give them the skills to grasp how statistical software works, tweak it to suit their needs, recombine existing pieces of code, and when needed create their own programs. Students will learn the core of ideas of programming (functions, objects, data structures, input and output, debugging, and logical design) through writing code to assist in numerical and graphical statistical analyses. Students will learn how to write maintainable code, and to test code for correctness. They will then learn how to set up stochastic simulations and how to work with and filter large data sets. Since code is also an important form of communication among scientists, students will learn how to comment and organize code to achieve reproducibility. Programming techniques and their application will be closely connected with the methods and examples presented in the co-requisite course. The primary programming package used in this course will be R. Prerequisite(s): None; familiarity with linear algebras is helpful. Corequisite(s): BIOSTAT 702. Credits: 3

BIOSTAT 722: Introduction to Statistical Programming II (SAS): This class is an introduction to programming in SAS, targeted at statistics majors with minimal programming knowledge, which will give them the skills to grasp how statistical software works, tweak it to suit their needs, recombine existing pieces of code, and when needed create their own programs. Students will learn the core of ideas of programming (data step, procedures, macros, ODS, input and output, debugging, and logical design) through writing code to assist in numerical and graphical statistical analyses. Students will learn how to write maintainable code, and to test code for correctness. They will then learn how to set up stochastic simulations and how to work with and filter large data sets. Since code is also an important form of communication among scientists, students will learn how to comment and organize code to achieve reproducibility. Programming techniques and their application will be closely connected with the methods and examples presented in the co-requisite course. The primary programming package focus used in this course will be SAS. Prerequisite(s): None; familiarity with linear algebra is helpful. Corequisite(s): BIOSTAT 705. Credits: 3

BIOSTAT 732: Independent Study: Independent Study is a semester long course focused on mentored research in the practice of biostatistics. Students work with an assigned mentor. This course is only open to students by permission of the Director of Graduate Studies. Credits: 1, 2, or 3

BIOSTAT 740: Continuation: Continuation is a semester-based, noncredit-bearing enrollment status used when a student is continuing scholarly activities with the same mentor. This course is only open to students by permission of the Director of Graduate Studies. Credits: 0

BIOSTAT 801: Biostatistics Career Preparation and Development I: The purpose of this course is to give the student a holistic view of career choices and development and the tools they will need to succeed as professionals in the world of work. The fall semester will focus on resume development, creating a professional presence, networking techniques, what American employers expect in the workplace, creating and maintaining a professional digital presence and learning how to conduct and succeed at informational interviews. Practicums in this semester include an informational interviewing and networking practicum with invited guests. Students participate in a professional “etiquette dinner” and a “dress for success” module as well an employer panel. Corequisite(s): BIOSTAT 701 through BIOSTAT 703. Credit: 1

BIOSTAT 802: Biostatistics Career Preparation and Development II: The purpose of this course is to further develop the student’s job seeking ability and the practical aspects of job/internship search or interviewing for a PHD program. The goal is to learn these skills once and use them for a lifetime. Modules that will be covered include: Communication skills both written and oral, interviewing with videotaped practice and review, negotiating techniques, potential career choices in the Biostatistics marketplace, and working on a team. This semester includes writing and interviewing practicum, and a panel of relevant industry speakers. Students will leave this course with the knowledge to manage their careers now and in the future. Prerequisite: BIOSTAT 801. Credit: 1

BIOSTAT 821: Software Tools for Data Science: A data scientist needs to master several different tools to obtain, process, analyze, visualize and interpret large biomedical data sets such as electronic health records, medical images, and genomic sequences. It is also critical that the data scientist masters the best practices associated with using these tools, so that the results are robust and reproducible. The course covers foundational tools that will allow students to assemble a data science toolkit, including the Unix shell, text editors, regular expressions, relational and NoSQL databases, and the Python programming language for data munging, visualization and machine learning. Best practices that students will learn include the Findable, Accessible, Interoperable and Reusable (FAIR) practices for data stewardship, as well as reproducible analysis with literate programming, version control and containerization. Prerequisite: BIOSTAT 721 and permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 822: R for Data Science: This course will build on the foundation laid in software tools for data science. The course will explore the flow of a typical data science project from importing, cleaning, transforming and visualizing datasets to modeling and communicating results, within the context of R programming. While the course will include best practices, syntax and idioms specific to R, the focus will be on the process of conducting analysis in a reproducible fashion, writing readable, well-documented code and creating a coherent presentation of results. Prerequisite: BIOSTAT 722 or BIOSTAT 821 or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 823: Statistical Program for Big Data: This course describes the challenges faced by analysts with the increasing importance of large data sets, and the strategies that have been developed in response to these challenges. The core topics are how to manage data and how to make computation scalable. The data management module covers guidelines for working with open data, and the concepts and practical skills for working with in-memory, relational and NoSQL databases. The scalable computing module focuses on asynchronous, concurrent, parallel and distributed computing, as well as the construction of effective workflows following DevOps practices. Applications to the analysis of structured, semi-structured and unstructured data, especially from biomedical contexts, will be interleaved into the course. The course examples are primarily in Python and fluency in Python is assumed. Prerequisite(s): BIOSTAT 821 or permission of the Director of Graduate Studies. Credits: 3

BIOSTAT 824: Case Studies in Biomedical Data Science: This course will highlight how biomedical data science blends the field of biostatistics with the field of computer science through the introduction of 3 to 5 case studies. Students will be introduced to analytic programs typically encountered in biomedical data science and will implement the data science and statistical skills introduced in their previous course work. Prerequisite(s): BIOSTAT 707, 821, 822, and 823 or permission of the Director of Graduate Studies. Credits: 3