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SPH BS 704: Introduction to Biostatistics
This course meets the biostatistics core course requirement for all degrees and concentrations at SPH. The course replaces BS701 and BS703. 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 analaysis; 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. This course gives students the skills to perform, present, and interpret basic statistical analyses using the R statistical package.
SPH BS 715: Practical Skills for Biostatistics Collaboration
Graduate Prerequisites: the MPH biostatistics core course or equivalent or approval of the instructor
This course will focus on skills required for effective research collaboration with investigators from various disciplines. Emphasis will be on the development of skills to communicate effectively with biostatistician and non‐biostatisticians collaborators, to write data collection and statistical analysisplans for grants, and/or publications, and to organize results in appropriate visual displays or tables. Other issues, including techniques to work efficiently in multi‐disciplinary research teams (e.g.,constructing timelines and deliverables) will also be discussed.
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: The epidemiology and biostatistics MPH core requirements 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: Successful completion of the biostatistics MPH core requirement or equivalent 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 BS805, BS820, BS821, BS851, BS852, BS853 and BS858.
SPH BS 728: Public Health Surveillance,a Methods Based Approach
Graduate Prerequisites: The MPH biostatistics core course or equivalent or permission of instructor is required. BS723 is strongly recommended.
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. Additionally students will propose a new surveillance system or modification of an existing system. This class carries Epidemiology concentration credit.
SPH BS 775: Applications of Statistical Methods in Clinical Research
Graduate Prerequisites: The biostatistics MPH core requirement and SPH BS723 or consent.
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
Graduate Prerequisites: BS723 or consent of the instructor. Students who did not earn a B grade or better in BS723 must meet with the BS805 faculty before registration.
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.
SPH BS 810: Meta-Analysis for Public Health & Medical Research
Graduate Prerequisites: The biostatistics and epidemiology MPH core course requirements and SPH BS723 or consent of instructor, email@example.com.
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.
SPH BS 821: Categorical Data Analysis
Graduate Prerequisites: The biostatistics MPH core requirement and BS723 or consent of instructor.
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
Graduate Prerequisites: SPH BS805 & linear algebra (CAS 142 or equivalent) or permission
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
Graduate Prerequisites: MPH biostatistics core course or BS723 required or consent of instructor (firstname.lastname@example.org). Recommended: Basic biology.
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
Graduate Prerequisites: The MPH biostatistics core core and SPH BS723 or consent of instructor
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 851: Applied Statistics in Clinical Trials I
Graduate Prerequisites: The MPH epidemiology and biostatistics core course requirements and SPH BS723 or consent of instructor, email@example.com
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
Graduate Prerequisites: The biostatistics and epidemiology core course requirements and BS723. Cannot be taken concurrently with BS805.
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. BS805 and BS852 cannot be taken in the same semester without approval from both faculty. Students should speak to faculty of both courses before registering.
SPH BS 853: Generalized Linear Models with Applications
Graduate Prerequisites: The biostatistics and epidemiology MPH core course requirements and BS805 or consent of instructor
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
Graduate Prerequisites: SPH BS851 or BS861 or consent of instructor
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.
SPH BS 855: Bayesian Modeling for Biomedical Research & Public Health
Graduate Prerequisites: BS805 or MA684 and MA581/MA582 or equivalent or consent
The purpose of this course is to present Bayesian modeling techniques in a variety of data analysis applications, including both hypothesis and data driven modeling. The course will start with an overview of Bayesian principles through simple statistical models that will be used to introduce the concept of marginal and conditional independence, graphical modeling and stochastic computations. The course will proceed with the description of advanced Bayesian methods for estimation of odds and risk in observational studies, multiple regression modeling, loglinear and logistic regression, latent class modeling including hidden Markov models and application to model-based clustering, graphical models and Bayesian networks. Applications from genetics, genomics, and observational studies will be included. These topics will be taught using real examples, class discussion and critical reading. Students will be asked to analyze real data sets in their homework and final project.
SPH BS 856: Adaptive Designs for Clinical Trials
Graduate Prerequisites: SPH BS851
An adaptive design is a clinical trial design that allows modification to aspects of the trial after its initiation without undermining the validity and integrity of the trial. Adaptive designs have become very popular in the pharmaceutical industry because they can increase the probability of success, considerably reduce the cost and time of the overall drug development process. With a recent rapid development in this area, there is a high demand for statisticians proficient in designing and conducting adaptive clinical trials. Students will learn different (both frequentist and Bayesian) adaptive designs and gain hands-on experiences on adaptive randomization, adaptive dose-finding, group sequential, and sample-size reestimation designs.