• 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: SPH PH 717; and BS723 or BS852; or consent of instructor
    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: SPH BS 723 or SPH BS 730; 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 and R programming languages.
  • 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 825: Advanced Methods in Infectious Disease Epidemiology
    Graduate Prerequisites: SPH EP 755; and BS730 or BS723; 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 830: Design and Analysis of Microarray Experiments and Next Generation Sequencing
    Graduate Prerequisites: MPH biostatistics core course or BS723 required or consent of instructor ( Recommended: Basic biology.
  • SPH BS 831: Genomics Data Mining and Statistics
    Graduate Prerequisites: Knowledge of basic statistics techniques (SPHBS704 or SPHPH717 or equivalent) and basic statistical computing skills using R (SPHBS730 or 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 Intermediate Biostatistics
    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 BS805 and BS852 rather than BS835, as students cannot take both BS835 and BS852 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.
  • SPH BS 845: Data Science and Statistical Modeling in R
    Graduate Prerequisites: SPH BS 730; 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 849: Bayesian Modeling for Biomedical Research & Public Health
    Graduate Prerequisites: At least one course of statistics to cover principles of probability and statistical inference, linear and logistic regression. Knowledge of R.
    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, hierarchical models, and latent class modeling including hidden Markov models and application to model-based clustering. 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 paper.
  • SPH BS 851: Applied Statistics in Clinical Trials I
    Graduate Prerequisites: SPH BS 723; or consent of instructor.
    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: SPH BS 723 or SPH BS 730;or consent of instructor. It is not recommended that BS805 and BS852 be taken concurrently, unless with the approval of the instructors of both courses.
    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. The course will use statistical software R and SAS. Students cannot take both BS852 and BS835.
  • SPH BS 853: Generalized Linear Models with Applications
    Graduate Prerequisites: SPH PH 717 and SPH BS 805; 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 BS 851 or SPH BS 861; 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
  • SPH BS 856: Adaptive Designs for Clinical Trials
    Graduate Prerequisites: SPH BS851; or consent of instructor
    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.
  • SPH BS 857: Analysis of Correlated Data
    Graduate Prerequisites: SPH BS 805 and SPH BS 852; or consent of instructor
    The purpose of this advanced seminar is to present some of the modern methods for analyzing tricorrelated observations. Such data may arise in longitudinal studies where repeated observations are collected on study subjects or in studies in which there is a natural clustering of observations, such as a multi-center study of observations clustered within families. Students start with a review of methods for repeated measures analysis of variance and proceed to more complicated study designs. The course presents both likelihood-based methods and quasi-likelihood methods. Marginal, random effects and transition models are discussed. Students apply these methods in homework assignments and a project.
  • SPH BS 858: Statistical Genetics I
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of instructor
    This course covers a variety of statistical applications to human genetic data, including collection and data management of genetic and family history information, and statistical techniques used to identify genes contributing to disease and quantitative traits in humans. Specific topics include basic population genetics, linkage analysis and genetic association analyses with related and unrelated individuals.
  • SPH BS 859: Applied Genetic Analysis
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of instructor.
    Statistical tools used to perform genetic association analysis are used to help unravel the genetic component of complex diseases. Investigators interested in the genetic analysis of complex traits need a basic understanding of the strengths and weaknesses of these methodologies. This course will provide the student with practical, applied experience in performing genome wide association analyses (GWAS) and in using the results of GWAS to better understand the biologic basis of disease. Additional special topics may include analysis of mitochondrial DNA and genetic methylation. Special emphasis is placed on understanding assumptions and issues related to statistical methodologies. The course is taught in a computer lab; in-class time will include didactic lecture and hands on applications using the linux BU shared computing cluster (SCC), R, and specialized genetics software for homework assignments.
  • SPH BS 860: Statistical Genetics II
    Graduate Prerequisites: SPH BS858 or BS859; or consent of instructor
    This course covers current topics in statistical genetics, with emphasis on how statistical techniques can be used with various types of genetics data to identify genes and genetic variants contributing to complex human diseases. Topics such as gene mapping in experimental organisms, advanced linkage analysis methods, statistical approaches for the analysis of genome-wide high density SNP scans in unrelated and family samples, post genome-wide association analyses and genetic risk prediction will be discussed.