The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.
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SPH BS 401: Survey in Biostatistical Methods
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 in June and July.
SPH BS 700: Essentials of Biostatistics
Graduate Prerequisites: MPH students cannot receive degree credit for BS700.
This intensive one-week course provides a comprehensive introduction to the use of biostatistics in the field of public health. Students learn to compute and interpret descriptive and inferential statistics. Topics include descriptive statistics and graphical displays of data, probability, confidence intervals, hypothesis testing for means and proportions, linear and logistic regression and survival analysis.
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.
SPH BS 715: Practical Skills for Biostatistics Collaboration
Graduate Prerequisites: the MPH biostatistics core course or equivalent or approval of the instructor
SPH BS 720: Introduction to R: software for statistical computing environment
Graduate Prerequisites: The MPH biostatistics core course requirement or the equivalent or permission of the instructor
Students will learn how to conduct statistical analysis using the open source 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 722: Design and Conduct of Clinical Trials
Graduate Prerequisites: SPH PH 717; 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 Computing
Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of instructor.
This course introduces students to statistical computing with focus on the SAS package. 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 equipment and software available through the Boston University Medical Center. This course is a prerequisite for these SPH courses: BS805, BS820, BS821, BS851, BS852, BS853 and BS858.
SPH BS 728: Public Health Surveillance,a Methods Based Approach
Graduate Prerequisites: SPH BS 723 or SPH BS 730; or permission of instructor are required.
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. This class carries Epidemiology concentration credit.
SPH BS 730: Introduction to R: software for statistical computing
Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of instructor.
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 740: Design and Conduct of Public Health Research
Graduate Prerequisites: SPHPH717 or consent of instructor.
This course provides practical experience with the theory and process of public health research. Topics include an overview of study design, principles of sampling and randomization, human subject issues and informed consent, the role of the IRB, qualitative research design and practice, and data management. This is a required course for the Design and Conduct of Public Health Research Certificate.
SPH BS 750: Essentials of Quantitative Data Management
Graduate Prerequisites: SPH BS723 Introduction to Statistical Computing
Any data analysis is only is good as the data on which it is based. This course will focus on the importance of high quality data and the skills required for effective data management, including collection, cleaning, auditing, and merging. Students will have hands-on experience with data sets. Examples of what can go wrong and how research can be complicated by or produce erroneous results due to poor quality data will be provided.
SPH BS 771: Topics in Biostatistics
Graduate Prerequisites: The biostatistics MPH core requirement.
Two and four credit topics courses may be offered throughout the academic year as a means of exploring new areas of study in the discipline. Topics vary by semester. Please refer to the print schedule for the specific course in any given semester. Not taught every year or semester.
SPH BS 790: Data Management in Public Health Research
Graduate Prerequisites: The biostatistics and epidemiology MPH core requirements and SPH BS723.
The goal of this course is to provide students with the skills required to design, organize and implement a data management system for public health research. This course is primarily geared to the data preparation stage of statistical analysis. Without high quality data, the statistical analysis is often difficult to carry out, and results of the statistical analysis can be invalid. Development of organizational tools, methods of data acquisition, data collection forms design, principles of database development, quality control of data, data security, and the role of technology will be discussed. Students will use Microsoft Access and SAS software packages to illustrate hands-on principles of data management. A mix of teaching methods will be employed: case studies of ?real world? examples from the instructors? and guest lecturers? research projects; readings from peer reviewed journals and chapters from several books; lectures; hands-on computer exercises; small group discussions.
SPH BS 795: Seminar in Ci
SPH BS 800: Accelerated Statistical Training
Graduate Prerequisites: Calculus I and II, including multivariable calculus, and linear algebra to cover matrix operations, matrix functions, and singular value decomposition.
This course is designed for the newly developed 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 the 3 weeks preceding the Fall semester, and will involve 15 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. Please note one year of calculus to include multivariable calculus and linear algebra are prerequisites for this course. 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 or SPH BS 700 or SPH BS 800; or consent of the 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 S will also provide students with basic computing skills to enroll to more advanced statistical classes such as BS830 and BS857.
SPH BS 805: Intermediate Statistical Computing and Applied Regression Analysis
Graduate Prerequisites: Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of the instructor. BS805 and BS806 cannot both be taken for credit. It is not recommended that BS805 and BS852 be taken concurrently. BS805
This course is a sequel to BS723. 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: Multivariable Analysis for Biostatisticians
Graduate Prerequisites: Cannot be taken concurrently with BS805. BS805 and BS806 cannot both be taken for credit. Calculus I and II, including multivariable calculus, and linear algebra to cover matrix operations, matrix fun
This course will focus on skills required for effective conduct of data analysis. 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, experimental design and multivariate analysis (PCA and cluster analysis) with particular emphasis on applications in medicine and public health.
SPH BS 810: Meta-Analysis for Public Health & Medical Research
Graduate Prerequisites: SPH BS 723 or SPH BS 730; 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 820: Logistic Regression and Survival Analysis
Graduate Prerequisites: The biostatistics and epidemiology MPH core course requirements and BS723 or BS852.
This course provides basic knowledge of logistic regression and analysis of survival data. Regression modeling of categorical or time-to-event outcomes with continuous and categorical predictors is covered. Checking of model assumptions, goodness of fit, use of maximum likelihood to determine estimates and test hypotheses, use of descriptive and diagnostic plots are emphasized. The SAS statistical package is used to perform analyses. Grading will be based on homework and exams.