Courses

The course descriptions below are correct to the best of our knowledge as of August 2011. Instructors reserve the right to update and/or otherwise alter course descriptions as necessary after publication. The listing of a course description here does not guarantee a course’s being offered in a particular semester. The Course Rotation Guide lists the expected semester a course will be taught. Paper copies are also available in the BUSPH Registrar’s office. Please refer to the published schedule of classes for confirmation a class is actually being taught and for specific course meeting dates and times.

• SPH BS 701: Elementary Biostatistics
  This course meets the biostatistics MPH core requirement and is for students who have not had prior experience with statistics or biostatistics. Topics include the collection, classification, and presentation of descriptive data; the rationale of estimation and hypothesis testing; correlation and regression analysis; analysis of variance; and analysis of contingency tables. Special attention is directed to the ability to recognize and interpret statistical procedures in articles from the current literature. Students will also learn statistical computing techniques using Microsoft Excel. Students who take this course cannot take BS703 for degree credit. This course or BS703 is required for all MPH students. Students may not take BS701 and BS703 for degree credit.
• SPH BS 703: Biostatistics
  This is the more advanced MPH biostatistics core course. This course is recommended for students concentrating in biostatistics, environmental health, or epidemiology, and for students with previous exposure to statistical methods or an interest in public health research methods. Topics include confidence intervals and hypothesis testing; sample size and power considerations; analysis of variance and multiple comparisons; correlation and regression; multiple regression and statistical control of confounding; logistic regression; and survival analysis. This course gives students the skills to perform, present, and interpret basic statistical analyses, using the R statistical computing package. For the more advanced topics, the focus is on interpretative skills and critically reading the literature. This course satisfies the core biostatistics requirement for MPH students. Biostatistics concentrators should take this course, though the course does not count towards the 16 required concentration credits. Students who take BS703 cannot take BS701 for degree credit.
• SPH BS 720: Introduction to R: software for statistical computing environment
  This course provides students an opportunity to use the public domain and free software, R to perform statistical computing. The R language provides a rich environment for working with data, especially for statistical modeling and graphics. Emphasis is on student data manipulation and basic statistical analysis including exploratory data analyses, classical tests of samples, categorical data analysis, and regression. Students will identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This is a hands-on, project based course to enable students to develop skills and to solve statistical problems using R. Class is two credits.
• SPH BS 722: Design and Conduct of Clinical Trials
  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
  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 BS805, BS820, BS821, BS851, BS852, BS853 and BS858.
• SPH BS 735: Quantitative Methods in Public Health Surveillance
  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 quasi-experimental designs, weighted sampling, and capture-recapture methods. 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 771: Topics in Biostatistics
  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 775: Applications of Advanced Statistical Methods in Clinical Research
  This course provides a non-technical (no computer programming) overview of concepts in statistical methods used for clinical research and their applications. Each week, students read a methodologic article and a clinical research article. The first portion of the class is a didactic presentation; the second portion is a discussion of the clinical research article, incorporating the concepts discussed in the didactic presentation. Students explore statistical test selection, alternative tests or approaches. Students examine interpretations of scientific articles in the lay press.
• SPH BS 805: Intermediate Statistical Computing and Applied Regression Analysis
  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, logistic regression, survival analysis, the analysis of correlated data, and statistical power. Includes a required lab section (BS805 B1 OR BS805 C1 for which students must register).
• SPH BS 810: Meta-Analysis for Public Health & Medical Research
  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
  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.
• SPH BS 821: Categorical Data Analysis
  This course focuses on the statistical analysis of categorical outcome data. Topics include the binomial and Poisson distributions, logistic and Poisson regression, nonparametric methods for ordinal data, smoothed regression modeling, the analysis of correlated categorical outcome data, cluster analysis, missing data and sample size calculations. The course emphasizes practical application and makes extensive use of the SAS programming language.
• SPH BS 822: Advanced Methods in Statistical Computing
  This course introduces advanced statistical methods and programming techniques that allow students to examine advanced statistical models that go beyond that available with standard SAS procedures taught in BS805. Topics include simulation studies, bootstrapping and Bayesian analysis. Students will apply these methods in homework assignments.
• SPH BS 830: Design and Analysis of Microarray Experiments and Next Generation Sequencing
  In this course, students will be presented with the methods for the analysis of gene expression data measured through microarrays. The course will start with a review of the basic biology of gene expression and an overview of microarray technology. The course will then describe the statistical techniques used to compare gene expression across different conditions and it will progress to describe the analysis of more complex experiments designed to identify genes with similar functions and to build models for molecular classification. The statistical techniques described in this course will include general methods for comparing population means, clustering, classification, simple graphical models and Bayesian networks. Methods for computational and biological validation will be discussed.
• SPH BS 845: Applied Statistical Modeling and Programming in R
  This course covers applications of modern statistical methods using R, a free and open source statistical computing package with powerful yet intuitive graphic tools. R is under more active development for new methods than other packages. We will first review data manipulation and programming in R, then cover theory and applications in R for topics such as linear and smooth regressions, survival analysis, mixed effects model, tree based methods, multivariate analysis, boot strapping and permutation.
• SPH BS 850: Advanced Statistical Methodology for the Computational Biosciences
  This course will discuss in depth advanced statistical computing methods used in scientific, especially biomedical, applications: generation of random numbers, optimization methods, numerical integration and advanced computational tools such as the EM algorithm, importance sampling, Gibbs sampler, Metropolis Hastings, auxiliary variable methods, data augmentation, and population-based Monte Carlo. The second half of the course will involve detailed discussions of statistical models and methods for problems in genomics and computational biology, including dynamic programming, hidden Markov models, multiple sequence alignment, phylogenetic tree reconstruction, RNA and protein structure analysis, gene regulatory network discovery and analysis of genome tiling array and high-throughput sequencing data. Computer programming exercises would apply the methods discussed in class, primarily using the software R. During the course, students will form small groups to select a topic of interest, on which they will carry out a course project implementing statistical computing methods appropriate for the application.
• SPH BS 851: Applied Statistics in Clinical Trials I
  This is an intermediate statistics course, focused on statistical issues applicable to analyzing efficacy data for clinical trials. Topics include design and analysis considerations for clinical trials, such as randomization and sample size determination, and the application of statistical methods such as analysis of variance, logistic regression and survival analysis to superiority and non-inferiority clinical trials. This course includes lectures and computer instructions. Upon completion of the course, the student will be able to have a working knowledge of how to collect and manage clinical trial data; will be to analyze continuous, dichotomous, and time-to-event clinical trial data; and will be able to contribute to the statistical portions of a clinical trial study design. The student will also gain the overall knowledge required to interpret clinical trial statistical results.
• SPH BS 852: Statistical Methods in Epidemiology
  This course covers study design and intermediate-level data analysis techniques for handling confounding in epidemiologic studies. Confounding is carefully defined and distinguished from interaction. Course content covers stratification and multivariable techniques for controlling confounding in both matched and independent sample study designs, including analysis of covariance, logistic regression, and proportional hazards models. Model fit and prediction are discussed. Students are required to apply these methods with the aid of computerized statistical packages.
• SPH BS 853: Generalized Linear Models with Applications
  This course introduces statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in health science research. The statistical models discussed include: Logistic regression for binary and binomial data, Nominal and Ordinal Multinomial logistic regression for multinomial data, Poisson regression for count data, and Gamma regression for data with constant coefficient of variation. All of these models are covered as special cases of the Generalized Linear Statistical Model, which provides an overarching statistical framework for these models. We will also introduce Generalized Estimating Equations (GEE) as an extension to the generalized models to the case of repeated measures data. The course emphasizes practical applications, making extensive use of SAS for data analysis.
• SPH BS 854: Bayesian Methods in Clinical Trials
  Bayesian statistical methods use prior information or beliefs, along with the current data, to guide the search for parameter estimates. In the Bayesian paradigm probabilities are subjective beliefs. Prior information/ beliefs are input as a distribution, and the data then helps refine that distribution. The choice of prior distributions, posterior updating, as well as dedicated computing techniques are introduced through simple examples. Bayesian methods for design, monitoring analysis for randomized clinical trials are taught in this class. These methods are contrasted with traditional (frequentist) methods. The emphasis will be on concepts. Examples are case studies from the instructors' work and from medical literature. R will be the main computing tool used.

Back to full list of School of Public Health