# Courses

The course descriptions below are correct to the best of our knowledge as of May 2014. 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. Please refer to the published schedule of classes for confirmation a class is actually being taught and for specific course meeting dates and times. In addition to the courses listed in the bulletin and courses approved after June 1, 2014, SPH, SPH degree candidates may register for a directed (independent) study with a full-time SPH faculty member. For more information, speak with your faculty advisor or a staff member in the SPH Registrar’s Office.

#### 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.

*2014FALLSPHBS704 B1, Sep 8th to Dec 15th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 6:00 pm | 8:30 pm | 670 | AUD |

*2014FALLSPHBS704 C1, Sep 2nd to Dec 16th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 1:00 pm | 3:30 pm | L | 112 |

*2014FALLSPHBS704 D1, Sep 4th to Dec 18th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 2:00 pm | 4:30 pm | L | BAKST |

*2014FALLSPHBS704 E1, Sep 5th to Dec 19th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

F | 2:00 pm | 4:30 pm | L | 110 |

*2015SPRGSPHBS704 A1, Jan 13th to May 5th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 6:00 pm | 8:30 pm |

*2015SPRGSPHBS704 B1, Jan 16th to May 1st 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

F | 2:00 pm | 4:30 pm |

#### Practical Skills for Biostatistics Collaboration

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.

*2015SPRGSPHBS715 A1, Feb 27th to Apr 3rd 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

F | 1:00 pm | 3:00 pm |

#### Introduction to R: software for statistical computing environment

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.

*2014FALLSPHBS720 A1, Sep 3rd to Oct 22nd 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 6:00 pm | 8:30 pm | L | 1110 |

*2015SPRGSPHBS720 A1, Jan 12th to Mar 16th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 6:00 pm | 8:45 pm |

#### 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.

*2014FALLSPHBS722 A1, Sep 2nd to Dec 16th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 2:00 pm | 4:45 pm | CT | 462 |

*2015SPRGSPHBS722 A1, Jan 15th to Apr 30th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 10:00 am | 12:45 pm |

#### 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.

*2014FALLSPHBS723 A1, Sep 8th to Dec 15th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 10:00 am | 12:45 pm | R | 107 |

*2014FALLSPHBS723 B1, Sep 2nd to Dec 16th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 6:00 pm | 8:45 pm | R | 107 |

*2014FALLSPHBS723 C1, Sep 3rd to Dec 17th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 10:00 am | 12:45 pm | L | 1110 |

*2014FALLSPHBS723 D1, Sep 4th to Dec 18th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 2:00 pm | 4:45 pm | R | 107 |

*2014FALLSPHBS723 E1, Sep 4th to Dec 18th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 6:00 pm | 8:45 pm | R | 107 |

*2015SPRGSPHBS723 A1, Jan 12th to May 4th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 10:00 am | 12:45 pm |

*2015SPRGSPHBS723 B1, Jan 12th to May 4th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 6:00 pm | 8:45 pm |

*2015SPRGSPHBS723 C1, Jan 13th to May 5th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 2:00 pm | 4:45 pm |

*2015SPRGSPHBS723 D1, Jan 13th to May 5th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 6:00 pm | 8:45 pm |

*2015SPRGSPHBS723 E1, Jan 14th to May 6th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 10:00 am | 12:45 pm |

*2015SPRGSPHBS723 F1, Jan 15th to Apr 30th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 6:00 pm | 8:45 pm |

#### Public Health Surveillance,a Methods Based Approach

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.

*2014FALLSPHBS728 A1, Sep 4th to Dec 18th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 2:00 pm | 3:30 pm | CT | 460 |

#### Applications of 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.

#### 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.

*2014FALLSPHBS805 A1, Sep 8th to Dec 15th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 6:00 pm | 8:45 pm | L | 112 |

*2014FALLSPHBS805 B1, Sep 2nd to Dec 16th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 6:00 pm | 8:45 pm | L | 212 |

*2015SPRGSPHBS805 A1, Jan 15th to Apr 30th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 6:00 pm | 8:45 pm |

#### 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.

*2014FALLSPHBS810 A1, Sep 3rd to Dec 17th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 2:00 pm | 4:45 pm | L | 210 |

#### 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.

*2015SPRGSPHBS820 A1, Jan 12th to May 4th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 6:00 pm | 8:45 pm |

#### 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.

*2014FALLSPHBS821 A1, Sep 4th to Dec 18th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

R | 6:00 pm | 8:45 pm | L | 201 |

#### 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.

*2015SPRGSPHBS822 A1, Jan 14th to May 6th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 6:00 pm | 8:45 pm |

#### 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.

#### 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.

*2014FALLSPHBS845 A1, Sep 8th to Dec 15th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 2:00 pm | 4:45 pm | L | 1110 |

#### 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.

*2015SPRGSPHBS851 A1, Jan 12th to May 4th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 2:00 pm | 4:45 pm |

#### 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. 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.

*2014FALLSPHBS852 A1, Sep 2nd to Dec 16th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 6:00 pm | 8:45 pm | 670 | 107/8 |

*2015SPRGSPHBS852 A1, Jan 12th to May 4th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

M | 2:00 pm | 4:45 pm |

#### 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.

*2015SPRGSPHBS853 A1, Jan 13th to May 5th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

T | 2:00 pm | 4:45 pm |

#### 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.

#### Bayesian Modeling for Biomedical Research & Public Health

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.

*2014FALLSPHBS855 A1, Sep 5th to Dec 19th 2014*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

F | 2:00 pm | 4:45 pm | L | 301 |

#### Adaptive Designs for Clinical Trials

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.

*2015SPRGSPHBS856 A1, Jan 14th to May 6th 2015*

Days | Start | End | Type | Bldg | Room |
---|---|---|---|---|---|

W | 6:00 pm | 8:45 pm |