Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• SPH BS 401S: Survey in Biostatistical Methods
  This course is offered through the Summer Institute in Biostatistics and is not for graduate credit. The objectives of this course are to introduce undergraduate students accepted to the program to biostatistics as a vibrant, vitally important discipline that provides essential tools for biomedical research and offers many exciting possibilities as a career. Students will learn the basic principles of biostatistical analysis, epidemiological analysis, design and analysis of clinical trials and statistical genetics. The course also includes an introduction to the SAS computing package and exposure to NHLBI studies of heart, lung, blood, and sleep disorders to illustrate the management, analysis and reporting of data. The class is offered June - July.
• SPH BS 704: Introduction to Biostatistics
  This course provides an overview of biostatistical methods, and gives students the skills to perform, present, and interpret basic statistical analyses. Topics include the collection, classification, and presentation of descriptive data; the rationale of estimation and hypothesis testing; analysis of variance; analysis of contingency tables; correlation and regression analysis; multiple regression, logistic regression, and the statistical control of confounding; sample size and power considerations; survival analysis. Special attention is directed to the ability to recognize and interpret statistical procedures in articles from the current literature. Students will use the R statistical package to analyze public health related data. * Can't be taken together for credit with SPH PH 717
• SPH BS 722: Design and Conduct of Clinical Trials
  Graduate Prerequisites: (SPHPH717) or consent of instructor. - This course covers the development, conduct, and interpretation of clinical trials. It is suitable for concentrators in any department. Topics include principles and practical features such as choice of experimental design, choice of controls, sample size determination, methods of randomization, adverse event monitoring, research ethics, informed consent, data management, and statistical analysis issues. Students write a clinical trial protocol during the semester.
• SPH BS 723: Introduction to Statistical Applications in SAS
  Graduate Prerequisites: (SPH PH 717 OR SPH BS 704 OR SPH BS 700 OR SPH BS 800) or consent of instructor. Can't be taken together for credit with SPH PH 760 or BS 730. This course introduces students to statistical computing with focus on the SAS software. Emphasis is on manipulating data sets and basic statistical procedures such as t-tests, chi-square tests, correlation and regression. Conditions underlying the appropriate use of these statistical procedures are reviewed. Upon completion of this course, the student will be able to use SAS to: read raw data files and SAS data sets, subset data, create SAS variables, recode data values, analyze data and summarize the results using the statistical methods enumerated above. This course includes hands-on exercises and projects designed to facilitate understanding of all the topics covered in the course. Students use software available through the Boston University Medical Center. This course is a prerequisite for these SPH courses: BS 805, BS 818, BS 819, BS 851, BS 852, BS 853 and BS 858.
• SPH BS 728: Public Health Surveillance, a Methods Based Approach
  Graduate Prerequisites: (SPH BS 723 OR SPH BS 730) or consent of instructor - Thacker wrote, "Surveillance is the cornerstone of public health practice." This course will provide an introduction to surveillance and explore its connections to biostatistics and public health practice. Topics will include complex survey design, weighted sampling, capture-recapture methods, time series analyses and basic spatial analyses. Students will learn about available surveillance data, how to analyze these data, and how to write about their findings.
• SPH BS 730: Introduction to Statistical Applications in R
  Graduate Prerequisites: (SPH PH 717 OR SPH BS 704 OR SPH BS 700 OR SPH BS 800) or consent of instructor. Cannot be taken together for credit with SPH PH 760 or BS 723. - Students will learn how to conduct statistical analysis using the public domain and free statistical software, R. Many public, private, and international organizations use R to conduct analysis, thus experience with R is a great skill to add to one's credentials. R offers flexibility, ranging from ease of writing code for simple tasks (e.g. using R as a calculator) to implementing complex analyses using cutting-edge statistical methods and models. Additionally, the R language provides a rich environment for working with data, especially for statistical modeling, graphics, and data visualization. This course will emphasize data manipulation and basic statistical analysis including exploratory data analysis, classical statistical tests, categorical data analysis, and regression. Students will be able to identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This hands-on and project-based course will enable students to develop skills to solve statistical problems using R. R can be used as an alternative or in addition to SAS (BS723). R is compatible with Apple OS, Windows, and Unix environments.
• SPH BS 751: Essentials of Quantitative Data Management
  Graduate Prerequisites: SPH BS723 or consent of instructor This course offers a comprehensive foundation in research data management, emphasizing the intersection between traditional data management practices and emerging data science approaches. Starting with the essentials of data planning and quality management, students will explore the full research data lifecycle, from initial data collection through to data sharing, secure storage, and regulatory compliance. In response to key concerns highlighted in the NIH’s 2018 Strategic Plan for Data Science, such as the need for standardized practices, data interoperability, and a skilled workforce, this course equips students with the practical skills to manage data responsibly and effectively. Core topics include developing a Data Management Plan (DMP) that integrates study protocol requirements, establishing robust quality control processes, and implementing regulatory- compliant procedures. Through hands-on experience, students will learn to design Case Report Forms, create secure electronic data capture systems, use programming for data cleaning, derive variables, and harmonize datasets in line with standards such as CDISC, HL7, and PhenX. Additional focus is given to data privacy, ethical considerations, data governance, and the FAIR principles to ensure that data is handled transparently and responsibly. By the end of the course, students will be prepared to contribute meaningfully to research and clinical data management, ensuring data integrity, reproducibility, and regulatory alignment.
• SPH BS 755: Theory of Linear Models in Biostatistics
  Graduate Prerequisites: CASMA581. Linear models are essential tools for analyzing data and widely used throughout science and society. This course will focus on linear models for biology and medicine, including a review of vectors and matrices, the multivariate normal distribution and other related distributions, least squares approach, inference for general linear model, linear hypothesis testing, model diagnostics, multiple linear regression, graphical and criteria-based variable selection, prediction, regularized regression techniques and Bayesian linear models. Students will implement the theory they learn by conducting a data analysis in R, applying the concepts and techniques covered in the course to real-world biostatistical data. Effective Fall 2023, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II, Teamwork/Collaboration.
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  • Quantitative Reasoning II
  • Teamwork/Collaboration
• SPH BS 800: Accelerated Statistical Training
  Graduate Prerequisites: One year of college-level calculus, including multivariable calculus, and linear algebra to cover matrix operations, matrix functions, and s ingular value decomposition. - This course is designed for the MS in Applied Biostatistics program and will cover concepts of descriptive statistics and exploratory data analysis, measures of association in epidemiological studies, probability, statistical inference and computing in R and SAS. It is intended to equip students enrolling in the MS in Applied Biostatistics program with sufficient probability, statistics and computing background to enter 800 levels courses and finish the MS program within a year. The course will be offered during 2 weeks preceding the Fall semester, and will involve 10 day-long modules. Modules will generally run from 10am to 5pm, combining a traditional lecture (10am to 12pm), a practice session in which students will practice the notions learned in class through exercises (1pm to 2:30pm), and a computer lab (3pm to 5pm) in which the students will learn basic computing in R and SAS and also apply the notions learned in class to real data. Allowing a student to waive this course is at the discretion of the MS in Applied Biostatistics program directors.
• SPH BS 803: Statistical Programming for Biostatisticians
  Graduate Prerequisites: ((SPH PH 717 or SPH BS 704) and SPH BS 723) or SPH BS 800; or consent of instructor. - This course will focus on skills required for advanced computing applications in biostatistics. Students will learn statistical programming and methods such as loops, functions, macros as well as data visualization techniques in SAS and R. Furthermore, the course will provide and introduction to Linux and basic statistical programming in Python. Lab sessions will also provide students with basic computing skills to enroll to more advanced statistical classes such as SPH BS 830 and SPH BS 857.
• SPH BS 805: Intermediate Programming in SAS for Applied Linear Models
  Graduate Prerequisites: (SPH BS 723) or consent of instructor. It is not recommended that SPH BS 805 and SPH BS852 be taken concurrently. * Can't be taken together for credit with SPH BS 806 - This course is a sequel to SPH BS 723. Emphasis is placed on the use of intermediate-level programming with the SAS statistical computer package to perform analyses using statistical models with emphasis on linear models. Computing topics include advanced data file manipulation, concatenating and merging data sets, working with date variables, array and do-loop programming, and macro construction. Statistical topics include analysis of variance and covariance, multiple linear regression, principal component and factor analysis, linear models for correlated data, and statistical power. Includes a required lab section.
• SPH BS 806: Statistical Learning with Applications in R
  Graduate Prerequisites: One year of college-level calculus, including multivariable calculus, and linear algebra to cover matrix operations, matrix functions and singular value decomposition. Can't be taken together for credit with BS 805. - This course will focus on skills required for effective conduct of data analysis with statistical packages, primary with R. This course will focus on the multiple regression modeling and multivariate analysis to cover multi-way ANOVA, multiple linear regression, classification and regression trees, automated model search, model fit and diagnostic, and multivariate analysis (PCA and cluster analysis) with particular emphasis on applications in medicine and public health.
• SPH BS 807: Applied Causial Inference in Health Research
  Graduate Prerequisites: ((SPH BS 723 OR SPH BS 730) AND (SPH BS 852 OR SPH EP 854)) or consent of instructor. The course requires experience with logistic regression and survival analysis, and SAS or R coding. - This is an advanced statistics course, focused on application of causal inference methods in medical research. Topics covered include counterfactual outcomes, causal diagrams, mediation analysis, instrumental variable, and g- methods to deal with time-varying confounding. This course includes lectures, computer instructions, and discussion of reading material.
• SPH BS 810: Meta-Analysis for Public Health & Medical Research
  Graduate Prerequisites: (SPHBS723 OR SPHBS730) or consent of instructor. - Meta-analysis is the statistical analysis of research findings and is widely used in public health and medical research. Typically meta-analysis is employed to provide summary results of the research in an area, but other uses include exploratory analyses to find types of subjects who best respond to a treatment or find study-level factors that affect outcomes. The course will cover the theory and use of the most common meta-analytic methods, the interpretation and limitations of results from these methods, diagnostic procedures, and some advanced topics with a focus on public health application. Grading will be based on homework, an exam and a project.
• SPH BS 818: Basic Survival Analysis
  Prerequisites: SPHBS 723 or 730. - The goal of the course is to prepare students to conduct rigorous analyses using basic approaches for time-to-event, or survival data. The course provides hands-on training using the SAS or R statistical software packages for comparing survival between groups, preparing adjusted comparisons, quantifying uncertainty, and predicting time-to-event outcomes. Also emphasized are the use of maximum likelihood to determine estimates and test hypotheses, use of descriptive and diagnostic plots.
• SPH BS 819: Logistic Regression
  Prerequisites: SPHBS 723 or 730. - This course provides basic knowledge of logistic regression for dichotomous outcomes, extensions of logistic regression to outcomes with a greater number of categories, and to regression models for count outcomes. The course focuses on prediction of categorical and count outcomes with emphasis on the use of maximum likelihood to determine estimates and test hypotheses, use of descriptive and diagnostic plots, and use of the SAS (or R) statistical package to perform analyses.
• SPH BS 825: Advanced Methods in Infectious Disease Epidemiology
  Graduate Prerequisites: (SPH EP 755 AND (SPH BS 730 or SPH BS 723)) or consent of instructor - This course aims to introduce students to statistical and mathematical methods used in infectious disease epidemiology. Students will be able to evaluate and appraise the literature in this field, be able to select which methods to use in different circumstances, implement some methods in simple situations and we will provide sufficient background reading that students can further examine methods that are of particular interest. This will be a hands-on course involving class discussions, computer lab sessions and a class debate on a controversial topic in infectious disease epidemiology.
• SPH BS 828: Advanced Survival Analysis
  Prerequisites: SPHBS 818 AND (CASMA 581 or 582). - The course will cover advanced topics in survival analysis, giving an overview of important analysis techniques that are students are likely to encounter in public health research with time-to-event data. The aim of the course will be to build a strong understanding of the theoretical foundations of the methods, while also connecting the theoretical methods to the real-world public health problems that require them. This combination will allow the students to move forward with both theoretical dissertation work in survival analysis and collaborative work requiring complex survival analysis applications in public health problems with longitudinal data. After taking the course, students will have: - An understanding of the theoretical concepts needed to understand and contribute to the survival analysis methodological literature, and the open areas of research in the field. - An ability to appropriately identify the issues that arise with time-to-event or survival data in public health research, and a toolkit for applying the appropriate methods to address them. The topics will include: - Counting processes and martingales: counting processes and martingale theory are a way to frame events occurring over the course of time and are commonly used tools to develop statistical methods for time-to-event data, and to prove their statistical properties. This is an important topic for students interested in including any survival analysis in their dissertation work to be able to understand and contribute to the literature. We will revisit some topics from prior survival courses using counting processes (ex. Logrank test, Cox proportional hazards model). - Joint modeling: joint modeling is used to assess how longitudinal covariates (common in public health research) affect a time to event outcome. - Restricted mean survival: An approach to summarizing survival data that is used as an alternative to the more commonly used proportional hazards model, and avoids some of its pitfalls. - Pseudo observations: this method transforms survival data with censoring to a dataset of pseudo observations that are not censored allowing for the use of traditional statistics methods outside of survival analysis. - Causal inference: causal inference with survival outcomes has unique challenges, including the interpretation of common summary measures like hazard ratios as causal quantities. - Recurrent Events: most of the methods that students have been exposed to will be for events that occur once (e.g. Death, hospital discharge). Recurrent event methods move a step further into methods that occur repeatedly (e.g. repeat infections, tumor regressions, repeated interactions with the healthcare system).
• SPH BS 831: Statistical Methods and Applications for Genomics
  Graduate Prerequisites: Knowledge of basic statistics techniques (SPHBS704 or SPHPH717 or equivalent) and basic statistical computing skills using R (SPHBS730 or e equivalent) or consent of instructor - The goal of this course is for the students to develop a good understanding and hands-on skills in the design and analysis of 'omics' data from microarray and high-throughput sequencing experiments, including data collection and management, statistical techniques for the identification of genes that have differential expression in different biological conditions, development of prognostic and diagnostic models for molecular classification, and the identification of new disease taxonomies based on their molecular profile. These topics will be taught using real examples, extensively documented hands- on work, class discussion and critical reading. Students will be asked to analyze real gene expression data sets in their homework and final project. Principles of reproducible research will be emphasized, and students will become proficient in the use of the statistical language R (an advanced beginners knowledge of the language is expected of the students entering the class) and associated packages (including Bioconductor), and in the use of R markdown (and/or electronic notebooks) for the redaction of analysis reports.
• SPH BS 835: Applied Biostatistical Methods for Public Health Practice
  Graduate Prerequisites: (SPH BS 723 OR SPH BS 730) or consent of instructor * Can't be taken together for credit with SPH BS 852 - Students with a strong interest in statistical programming and a strong mathematical background are encouraged to take SPH BS 805 and SPH BS 852 rather than SPH BS 835, as students cannot take both SPH BS 835 and SPH BS 852 for credit. This course covers intermediate-level statistical methods commonly used in epidemiologic and public health research. The course has an applied focus, with emphasis on understanding research questions addressed by these methods, key assumptions these analyses rely on, and the presentation and interpretation of results. Students will use either the SAS or R statistical package to carry out analyses. Topics include multivariable regression models for continuous, binary, survival, and longitudinal outcome data, stratified and matched analyses of epidemiologic data, and analysis of survival data. This course will provide the student with training in intermediate level biostatistical analyses and the use of biostatistical software.