Biostatistics & Data Science

Course Director: Karla Ballman, Ph.D.

This course provides an introduction to important topics in biostatistical concepts and reasoning. Specific topics include tools for describing central tendency and variability in data, probability distributions, sampling distributions, estimation, and hypothesis testing. Assignments will involve computation using the R programming language.

Course Director: Oleksandr Savenkov, Ph.D.

The course will describe methods related to categorical data analysis and basic concepts for censored data and Kaplan-Meier; and learn how to select appreciate methods and how to interpret the results from categorical data analysis and Kaplan-Meier.

Course Director: Elizabeth Sweeney, Ph.D.

This course provides an introduction to data science using both the R and python programming languages. In this course students will gain experience working directly with data to pose and answer questions. The course will be divided into two parts; the first part will be taught with the programming language R and the second with python. Topics covered include: reproducible research, exploratory data analysis, data manipulation, data visualization techniques, simulation design, and unsupervised learning methods.

Course Director: Yiye Zhang , Ph.D., M.S.

This course is designed to introduce students to the fundamentals of health services research. Health services research is the discipline that measures the evaluations of interventions designed to improve healthcare. These interventions can include changes to the organization, delivery and financing of health care and various healthcare policies. Common outcome measures in health services research include (but are not limited to) patient safety, healthcare quality, healthcare utilization, and cost. Specific topics to be covered in this course include: refining your research question, identifying common research designs and their strengths and weaknesses, minimizing bias and confounding, selecting data sources, optimizing measurement, and more. There will also be a component of the course that explores how to present your ideas and iteratively refine your work, based on feedback from peers and reviewers. This course includes both lectures and interactive group discussions. Students will be able to apply the methods learned in this course to their masters’ research projects.

Course Director: Nathaniel Hupert, M.D., MPH

Every component of health care delivery, from patient scheduling and bed management to information utilization and logistics, is amenable to improvement using approaches based on operations research (OR), the branch of engineering that calls itself “the science of better.”  This course will introduce students to key concepts and methods in OR, including queuing theory, simulation, and optimization.  Applications using common spreadsheet software and/or free online modeling applications will be emphasized. Student teams will then use these tools to design an efficient, high-performance outpatient clinic.

Course Director: Zhengming Chen, Ph.D., MPH, M.S.

This course provides introduction to the statistical software SAS. Students will receive a hands-on exposure to data management and report generation with one of the most popular statistical software packages.

Course Director: Linda Gerber, Ph.D.

The course will describe and apply measures of disease incidence and prevalence, and measures of effect; explain the basic principles underlying different study designs, including descriptive, ecological, cross-sectional, cohort, case-control and intervention studies; assess strengths and limitations of different study designs; identify problems interpreting epidemiological data: chance, bias, confounding and effect modification; address validity, intra-rater reliability and inter-rater reliability.

Course Director: Fei Wang, Ph.D.

Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.

Course Director: Samprit Banerjee, Ph.D., MStat 

There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.

Course Director: Samprit Banerjee, Ph.D., MStat 

Prerequisite: Biostatistics I

The focus of this course is theory and application of different types of regression analysis. Topics will include: linear regression, logistic regression, and cox proportional hazards regression. Additional topics will include coding of explanatory variables, residual diagnostics, model selection techniques, random effects and mixed models, and maximum likelihood estimation. Homework assignments will involve computation using the R statistical package.

Course Director: Kathy (Xi) Zhou, Ph.D.

The course covers topics important in the application of statistical methods and relevant statistical software packages (primarily R) to biomedical studies, with an emphasis on applications in the design and analysis related to biomedical experiments, clinical trials and observational studies. The course uses real-world case studies to introduce commonly used experimental designs in biomedical research and discuss a variety of statistical methods and analytic tools for analyzing data generated from those studies. The course promotes good statistical/analytical practice through the introduction of several widely adopted reporting guidelines and tools for carrying out reproducible data analysis. The course aims to help students develop expertise in applying statistical methods and analytical tools, including developing their own R packages, to solve the design and data analysis challenges in biomedical studies and beyond.

Course Director: Mark Unruh, Ph.D.

Addresses challenges in the use of electronic clinical data for research purposes, such as electronic health records, clinical data warehouses, electronic prescribing, clinical decision support systems and health information exchange. Students will learn how clinical processes generate data in these different systems, the tasks required to obtain data for research purposes and steps to prepare data for analysis. Examples of research uses of clinical data will be drawn from case studies in the literature. Students will acquire skills in data review, preparation and analysis through hands-on experience with clinical data.

Course Director: Arindam RoyChoudhury, Ph.D.

Pharmaceutical studies use many statistical methods that are not routinely taught as part of conventional biostatistics courses. In this course, the students will learn the statistical methods specifically used in pharmaceutical studies.

The course is divided into three modules.

The first module: “Statistical Aspects of Phase I Clinical Trial” will include 3+3 Design, accelerated titration; up and down designs;  continual reassessment method (CRM), Modified CRM, TITE CRM, Bayesian Logistic Regression Model (BLRM), escalation with overdose control (EWOC), toxicity probability interval (TPI) and modified TPI (mTPI).

The second module: “Statistical Aspects of Phase II Clinical Trial” will include design and analyses for One stage and Simon’s Two Stage Designs, Multi-arm Phase II design.

The third module: “Statistical Aspects of Phase III Clinical Trial” will include randomization, design and analysis for parallel, crossover, factorial, seamless Phase II/III, Adaptive and SMART designs.

Course Director: Samprit Banerjee, Ph.D., MStat 

The course starts with logistic regression and discriminant analysis with emphasis on classification and prediction. This course would cover some of more advanced topics such as regularized regression, resampling methods, tree-based methods and support vector machines.

Course Director: Arindam RoyChoudhury, Ph.D.

An independent biostatistician often encounters data collected on patients over a length of time, or data that are otherwise clustered. This course will give the students necessary tools to analyze such data, while building on the core biostatistics material they have learned from other courses. Specifically, the students will learn to use mixed-effect models, mixed-effect ANOVA, generalized linear mixed models (GLMM), mixed-effect Cox-regression, Bayesian hierarchical models, repeated measure and longitudinal data analysis with appropriate covariance structures.

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client asCou well as faculty and fellow classmates.

Course Director: Ivan Diaz, Ph.D.

The goal of this course is to introduce a core set of modern statistical concepts and techniques to the students, and to demonstrate how to use them to answer complex research questions in healthcare. The students will acquire knowledge on causal inference methods using machine learning, including directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, survival analysis, marginal structural models, longitudinal data, mediation analyses, effect modification, and precision medicine. This course will use the free software R to perform all statistical analysis.