Course Offerings

 

Below are the courses offered by the Department of Biostatistics.  

Fall 2014 Biostatistics Courses
PHP1501 - Essentials of Data Analysis
Professor Roee Rutman
T Th: 1:00 - 2:20 pm
Syllabus

Course Description:
This course covers the basic concepts of statistics and the statistical methods most commonly used in the social sciences and public health with an emphasis on application of methods to real data. The first half of the course introduces descriptive statistics and the inferential statistical methods of confidence intervals and significance tests, applied to means and proportions. The second half introduces bivariate and multivariate methods, emphasizing contingency table analysis, regression, and analysis of variance. This is designed to be a first course in Statistics, so know previous knowledge of the subject is expected. There are no prerequisites. 

CRN: 1637
PHP2507 - Biostats & Applied Data
Professor Annie Gjelsvik and Crystal Linkletter
W: 6:00 - 8:00 pm
Th: 1:00 - 2:20 pm
Syllabus

Course Desciption:
The objective of the year long, two-course sequence is for students to develop the knowledge, skills and perspectives necessary to analyze data in order to answer a public health questions. The year long sequence will focus on statistical principles as well as the applied skills necessary to answer public health questions using data, including: data acquisition, data analysis, data interpretation and the presentation of results. Through lectures, labs and small group discussions, this fall semester course will focus on identifying public health data sets, refining research questions, univariate and bivariate analyses and presentation of initial results. Prerequisite: understanding of basic math concepts and terms; basic functional knowledge of Stata. Enrollment limited to 50 MPH, CTR, and BSSI students. Instructor permission required.

CRN: 15972
PHP2510 - Princ. of Biostats & Data Analysis
Professor Cici Bauer
T Th: 9:00-10:20am
Syllabus

Course Description:
Comprehensive overview of methods for inference from censored event time data, with emphasis on nonparametric and semiparametric approaches. Topics include nonparametric hazard estimation, semiparametric proportional hazards models, frailty models, multiple event processes, with application to biomedical and public health data. Computational approaches using statistical software are emphasized. Prerequisites: PHP 2510 and 2511, or equivalent. Open to advanced undergraduates with permission from the instructor.

CRN: 15982
PHP2520 - Statistical Inference I
Professor Zhijin Wu
MW: 9:00-10:20 am
Syllabus
Course Desciption:
First of two courses that provide a comprehensive introduction to the theory of modern statistical inference. PHP 2520 presents a survey of fundamental ideas and methods, including sufficiency, likelihood based inference, hypothesis testing, asymptotic theory, and Bayesian inference. Measure theory not required. Open to advanced undergraduates with permission from the instructor.

CRN: 15980
PHP2602 - Analysis of Lifetime Data
Professor Xi Luo
T Th: 2:30-3:50 pm
Syllabus
Course Desciption:
Comprehensive overview of methods for inference from censored event time data, with emphasis on nonparametric and semiparametric approaches. Topics include nonparametric hazard estimation, semiparametric proportional hazards models, frailty models, multiple event processes, with application to biomedical and public health data. Computational approaches using statistical software are emphasized. Prerequisites: PHP 2510 and 2511, or equivalent. Open to advanced undergraduates with permission from the instructor.

CRN: 15982
PHP2601 - Linear & Generalized Linear Models
Professor Eunhee Kim
T Th: 1:00-2:20 pm
Syllabus
Course Description:
This course will focus on the theory and applications of linear models for continuous responses. Linear models deal with continuously distributed outcomes and assume that the outcomes are linear combinations of observed predictor variables and unknown parameters, to which independently distributed errors are added. Topics include matrix algebra, multivariate normal theory, estimation and inference for linear models, and model diagnostics. Prerequisites: APMA 1650 or 1660, or taking PHP 2520 concurrently.

Note: The course will cover fundamental and advanced topics in linear models, and concepts related to the generalized linear models will not be covered during the course.

CRN: 15984
PHP2610 - Casual Inference and Missing Data
Professor Joseph Hogan
T Th: 9:00-10:20 am
Syllabus
Course Description:
Systematic overview of modern statistical methods for handling incomplete data and for drawing causal inferences from "broken experiments" and observational studies. Topics include modeling approaches, propensity score adjustment, instrumental variables, inverse weighting methods and sensitivity analysis. Case studies used throughout to illustrate ideas and concepts. Prerequisite: Undergraduates: MATH 1610 or Graduates: PHP 2511. Open to advanced undergraduates with permission from the instructor.

CRN: 15986
PHP2550 - Practical Data Analysis
Professor Christopher Schmid
MW: 10:30-11:50 am
Syllabus
Course Description:
Covers practical skills required for successful analysis of scientific data including statistical programming, data management, exploratory data analysis, model fitting and checking, simulation, missing data and proper interpretation and presentation of results. Tools will be developed through a series of case studies based on different types of data requiring a variety of statistical methods. The R programming environment will be emphasized, although students may use other packages. Upon completion of the course, students should be able to manipulate, program, analyze, display and present data and statistical models so that they are comprehensible for the non-statistical expert scientific collaborator. Students should have courses in probability and statistical inference at the level of Math 1610 and PHP 2510 as well as regression analysis at the level of PHP 2511. Some familiarity with the R programming language or some other statistical programming language or some other statistical programming language is advisable.

CRN: 15987

 

Spring 2014 Biostatistics Courses
Course
Code
Title Schedule Instructor Course Description
PHP2508

Biostats & Applied Data Analysis II

 

W 6:00-8:00Th 1:00-2:20
Annie Gjelsvik
Crystal Linkletter
Biostatistics and Applied Data Analysis II is the second course in a year-long, two-course sequence designed to develop the skills and knowledge to use data to address public health questions. The courses are specifically for students in the Brown MPH program, and the training programs in Clinical and Translational Research. The sequence is completed in one academic year, not split across two years. The courses focus on statistical principles as well as the applied skills necessary to answer public health questions using data, including: acquisition, analysis, interpretation and presentation of results. Prerequisite: PHP 2507. Enrollment limited to 48. Instructor permission required.
CRN:  24847
PHP2511

Applied Regression Analysis

Syllabus

TTh 9:00-10:20 Roee Gutman Applied multivariate statistics, presenting a unified treatment of modern regression models for discrete and continuous data. Topics include multiple linear and nonlinear regression for continuous response data, analysis of variance and covariance, logistic regression, Poisson regression, and Cox regression. Prerequisite: APMA 1650 or PHP 2510. Open to advanced undergraduates with permission from the instructor.
CRN: 24848
PHP2540

Advanced Methods for Multivariate Analysis

Syllabus

MW 8:30-9:50 Eunhee Kim Survey of modern statistical methods for analysis of multivariate and high-dimensional data. Topics include inference for multivariate normally distributed data, methods for data reduction, classification and clustering, multiple comparisons for high-dimensional data, analysis of multidimensional contingency tables, and functional data analysis. Applications to diverse areas of scientific research, such as genomics, biomarker evaluation, and neuroscience will be featured. Prerequisites: APMA 1650 and 1660; or PHP 2520. Open to advanced undergraduates with permission from the instructor.
CRN: 24849
PHP2580

Statistical Inference II

Syllabus

MW 10:00-11:20 Constantine Gatsonis This sequence of two courses provides a comprehensive introduction to the theory of modern inference. PHP 2580 covers such topics as non-parametric statistics, quasi-likelihood, resampling techniques, statistical learning, and methods for high-dimensional Bioinformatics data. Prerequisite: PHP 2520. Open to advanced undergraduates with permission from the instructor.
CRN: 24850
PHP2602 Analysis of Lifetime Data TTh 2:30-3:50 Xi Luo Comprehensive overview of methods for inference from censored event time data, with emphasis on nonparametric and semiparametric approaches. Topics include nonparametric hazard estimation, semiparametric proportional hazards models, frailty models, multiple event processes, with application to biomedical and public health data. Computational approaches using statistical software are emphasized. Prerequisites: PHP 2510 and 2511, or equivalent. Open to advanced undergraduates with permission from the instructor.
CRN: 24851
PHP2604

Statistical Methods for Spatial Data

Syllabus

MW 1:00-2:20 Cici Bauer This course covers a variety of topics for spatial data, including data visualization, Bayesian hierarchical models, spatial models, as well as the computation techniques and statistical software to implement these models. Examples of applications will include, but are not limited to, spatial modeling of data from epidemiology, environmental studies and social sciences. Prerequisites: APMA 1650-1660 or PHP 2510-2511, and MATH 0520; some experience with scientific computing.
CRN:  24852
PHP2620 Statistical Methods in Bioinformatics, I TTh 10:30-11:50 Zhijin Wu Introduction to statistical concepts and methods used in selected areas of bioinformatics. Organized in three modules, covering statistical methodology for: (a) analysis of microarray data, with emphasis on application in gene expression experiments, (b) proteomics studies, (c) analysis of biological sequences. Brief review and succinct discussion of biological subject matter will be provided for each area. Available software will be introduced. Intro level statistics (PHP 2507/2508 or PHP 2510/2511) recommended. Other students should contact instructor. Intro to software R and Bioconductor tools provided in lab. Open to advanced undergraduates with permission from the instructor.
CRN: 24853
PHP2690F

Advanced Topics in Statistics:  Statistical Computing

Syllabus

TTh 1:00-2:20 Chris Schmid Covers the theory and application of common algorithms used in statistical computing including numerical analysis, random number generation, sorting, root finding, optimization, numerical integration, simulation and Monte Carlo methods, smoothing and density estimation, Markov chain Monte Carlo and bootstrapping. Some specific topics discussed include: rejection sampling, Newton-Raphson, Sweep, Gaussian quadrature, EM, importance sampling, Metropolis-Hastings, Gibbs sampling, kernel densities, maximum likelihood, simplex algorithm, etc. Necessary numerical linear algebra and analysis will be reviewed. Also discusses applications of these algorithms to real research problems. Recommended course work in multivariable calculus, linear algebra, and statistics (PHP 2510, 2511).
CRN:  26156
Fall 2013 Biostatistics Courses
Course
Code
Title Schedule Instructor Course Description
PHP2507 Biostats & Applied Data Analysis I Th: 1:00-2:30
W: 6:00-8:00
Annie Gjelsvik
Crystal Linkletter
The objective of the year long, two-course sequence is for students to develop the knowledge, skills and perspectives necessary to analyze data in order to answer a public health questions. The year long sequence will focus on statistical principles as well as the applied skills necessary to answer public health questions using data, including: data acquisition, data analysis, data interpretation and the presentation of results. Through lectures, labs and small group discussions, this fall semester course will focus on identifying public health data sets, refining research questions, univariate and bivariate analyses and presentation of initial results. Prerequisite: understanding of basic math concepts and terms; basic functional knowledge of Stata. Enrollment limited to 50 MPH, CTR, and BSSI students. Instructor permission required.

CRN: 15589
PHP2510

Princ. of Biostats & Data Analysis

Syllabus

T Th: 9:00-10:20 Cici Bauer Intensive first course in biostatistical methodology, focusing on problems arising in public health, life sciences, and biomedical disciplines. Summarizing and representing data; basic probability; fundamentals of inference; hypothesis testing; likelihood methods. Inference for means and proportions; linear regression and analysis of variance; basics of experimental design; nonparametrics; logistic regression. Open to advanced undergraduates with permission from the instructor.

CRN: 15591
PHP2520

Statistical Inference I

Syllabus

T Th: 2:30-3:50 Joseph Hogan First of two courses that provide a comprehensive introduction to the theory of modern statistical inference. PHP 2520 presents a survey of fundamental ideas and methods, including sufficiency, likelihood based inference, hypothesis testing, asymptotic theory, and Bayesian inference. Measure theory not required. Open to advanced undergraduates with permission from the instructor.

CRN: 15592
PHP2530

Bayesian Statistical Methods

Syllabus

MW: 9:00-10:20 Roee Gutman Surveys the state of the art in Bayesian methods and their applications. Discussion of the fundamentals followed by more advanced topics including hierarchical models, Markov Chain Monte Carlo, and other methods for sampling from the posterior distribution, robustness, and sensitivity analysis, and approaches to model selection and diagnostics. Features nontrivial applications of Bayesian methods from diverse scientific fields, with emphasis on biomedical research. Prerequisites: APMA 1650, PHP 2510, PHP 2511, or equivalent. Open to advanced undergraduates with permission from the instructor.

CRN: 15593
PHP2601

Linear & Generalized Linear Models

Syllabus

Th: 1:00-2:20
TTh: 1:00-2:20
Xi (Rossi) Luo Generalized linear models provide a unifying framework for regression. Important examples include linear regression, log-linear models, and logistic regression. GLMs for continuous, binary, ordinal, nominal, and count data. Topics include model parameterization, parametric and semiparametric estimation, and model diagnostics. Methods for incomplete data are introduced. Computing with modern software is emphasized. Prerequisites: APMA 1650 or PHP 2520. Open to advanced undergraduates with permission from the instructor.

CRN: 15594
PHP2690D

Advanced Topics in Biostatistics: Tools for Data Analysis

Syllabus

MW: 10:30-11:50 Chrisopher Schmid Designed for graduate and advanced undergraduate students who will be analyzing data and want to develop a practical hands-on toolkit. Topics including data collection and management, exploratory data analysis, fitting and checking models, simulation, handling missing data and presentation of results will be developed through a series of case studies based on different types of data requiring a variety of statistical methods. Statistical programming techniques including functions, graphs and tables will be emphasized. Students should have familiarity with basic concepts of statistics through regression. Permission of instructor required.

CRN: 16180

To view all public health courses, please visit the Brown's Course site.