Biostatistics

Introduction to Biostatistics

SPH BS 704 (3 credits)

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.

2016FALLSPHBS704 A1, Sep 6th to Dec 20th 2016
Days Start End Type Bldg Room
T 1:00 pm 3:30 pm L112
2016FALLSPHBS704 B1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
R 6:00 pm 8:30 pm E111
2017SPRGSPHBS704 A1, Jan 24th to May 9th 2017
Days Start End Type Bldg Room
T 6:00 pm 8:30 pm E111

Practical Skills for Biostatistics Collaboration

SPH BS 715 (1 credits)

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.

Design and Conduct of Clinical Trials

SPH BS 722 (4 credits)

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.

2016FALLSPHBS722 A1, Sep 6th to Dec 20th 2016
Days Start End Type Bldg Room
T 2:00 pm 5:00 pm 462
2017SPRGSPHBS722 A1, Jan 19th to May 4th 2017
Days Start End Type Bldg Room
R 10:00 am 1:00 pm CT 460A

Introduction to Statistical Computing

SPH BS 723 (4 credits)

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.

2016FALLSPHBS723 A1, Sep 12th to Dec 19th 2016
Days Start End Type Bldg Room
M 10:00 am 1:00 pm R107
2016FALLSPHBS723 B1, Sep 6th to Dec 20th 2016
Days Start End Type Bldg Room
T 6:00 pm 9:00 pm R107
2016FALLSPHBS723 C1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
R 2:00 pm 5:00 pm R107
2016FALLSPHBS723 D1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
R 6:00 pm 9:00 pm R107
2017SPRGSPHBS723 A1, Jan 23rd to May 8th 2017
Days Start End Type Bldg Room
M 6:00 pm 9:00 pm L110
2017SPRGSPHBS723 B1, Jan 24th to May 9th 2017
Days Start End Type Bldg Room
T 2:00 pm 5:00 pm R107
2017SPRGSPHBS723 C1, Jan 24th to May 9th 2017
Days Start End Type Bldg Room
T 6:00 pm 9:00 pm R107
2017SPRGSPHBS723 D1, Jan 25th to May 10th 2017
Days Start End Type Bldg Room
W 10:00 am 1:00 pm R107
2017SPRGSPHBS723 E1, Jan 19th to May 4th 2017
Days Start End Type Bldg Room
R 6:00 pm 9:00 pm R107
2017SPRGSPHBS723 F1, Jan 19th to Apr 27th 2017
Days Start End Type Bldg Room
R 2:00 pm 5:00 pm R107

Public Health Surveillance,a Methods Based Approach

SPH BS 728 (2 credits)

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.

2016FALLSPHBS728 A1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
R 2:00 pm 3:30 pm 460

Introduction to R: software for statistical computing

SPH BS 730 (4 credits)

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.

2016FALLSPHBS730 A1, Sep 9th to Dec 16th 2016
Days Start End Type Bldg Room
F 2:00 pm 5:00 pm L111
2017SPRGSPHBS730 A1, Jan 23rd to May 8th 2017
Days Start End Type Bldg Room
M 10:00 am 1:00 pm R107

Design Ph Res

SPH BS 740 (4 credits)

2017SPRGSPHBS740 A1, Jan 23rd to May 8th 2017
Days Start End Type Bldg Room
M 6:00 pm 9:00 pm L212

Essentials of Quantitative Data Management

SPH BS 750 (2 credits)

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.

2017SPRGSPHBS750 A1, Jan 25th to Mar 15th 2017
Days Start End Type Bldg Room
W 2:00 pm 5:00 pm R107

Applications of Statistical Methods in Clinical Research

SPH BS 775 (4 credits)

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

SPH BS 805 (4 credits)

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.

2016FALLSPHBS805 A1, Sep 12th to Dec 19th 2016
Days Start End Type Bldg Room
M 6:00 pm 9:00 pm L112
2016FALLSPHBS805 B1, Sep 6th to Dec 20th 2016
Days Start End Type Bldg Room
T 6:00 pm 9:00 pm L212
2017SPRGSPHBS805 A1, Jan 19th to May 4th 2017
Days Start End Type Bldg Room
R 6:00 pm 9:00 pm L112

Meta-Analysis for Public Health & Medical Research

SPH BS 810 (4 credits)

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.

2016FALLSPHBS810 A1, Sep 7th to Dec 21st 2016
Days Start End Type Bldg Room
W 2:00 pm 5:00 pm L210

Logistic Regression and Survival Analysis

SPH BS 820 (4 credits)

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.

2017SPRGSPHBS820 A1, Jan 23rd to May 8th 2017
Days Start End Type Bldg Room
M 6:00 pm 9:00 pm 201

Categorical Data Analysis

SPH BS 821 (4 credits)

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.

2016FALLSPHBS821 A1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
R 6:00 pm 9:00 pm L201

Advanced Methods in Statistical Computing

SPH BS 822 (4 credits)

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.

Advanced Methods in Infectious Disease Epidemiology

SPH BS 825 (2 credits)

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.

Design and Analysis of Microarray Experiments and Next Generation Sequencing

SPH BS 830 (4 credits)

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

SPH BS 845 (4 credits)

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.

2016FALLSPHBS845 A1, Sep 7th to Dec 21st 2016
Days Start End Type Bldg Room
W 10:00 am 1:00 pm L111

Applied Statistics in Clinical Trials I

SPH BS 851 (4 credits)

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.

2017SPRGSPHBS851 A1, Jan 23rd to May 8th 2017
Days Start End Type Bldg Room
M 2:00 pm 5:00 pm L211

Statistical Methods in Epidemiology

SPH BS 852 (4 credits)

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.

2016FALLSPHBS852 A1, Sep 6th to Dec 20th 2016
Days Start End Type Bldg Room
T 6:00 pm 9:00 pm 107/
2016FALLSPHBS852 B1, Sep 8th to Dec 15th 2016
Days Start End Type Bldg Room
M 2:00 pm 5:00 pm 462
2017SPRGSPHBS852 A1, Jan 19th to May 4th 2017
Days Start End Type Bldg Room
R 6:00 pm 9:00 pm L110

Generalized Linear Models with Applications

SPH BS 853 (4 credits)

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.

2017SPRGSPHBS853 A1, Jan 24th to May 9th 2017
Days Start End Type Bldg Room
T 2:00 pm 5:00 pm R115