If you have yet to register for Spring courses, here are some Biostatistics courses offered:
SPH BS 720 – Introduction to R: software for statistical computing environment
This course provides students an opportunity to use the public domain and free software, R to perform statistical computing. The R language provides a rich environment for working with data, especially for statistical modeling and graphics. Emphasis is on student data manipulation and basic statistical analysis including exploratory data analyses, classical tests of samples, categorical data analysis, and regression. Students will identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This is a hands-on, project based course to enable students to develop skills and to solve statistical problems using R. Class is two credits.
[ 2 cr.] Ching-Ti Liu M 6-845pm does not count towards MA or PhD elective credit
SPH BS 845 – Applied Statistical Modeling and Programming with 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.
[ 4 cr.] Qiong Yang W 6-845pm
SPH BS 850 – Advanced Statistical Methodology for the Computational Biosciences
This course will discuss in depth advanced statistical computing methods used in scientific, especially biomedical, applications: generation of random numbers, optimization methods, numerical integration and advanced computational tools such as the EM algorithm, importance sampling, Gibbs sampler, Metropolis Hastings, auxiliary variable methods, data augmentation, reversible jump MCMC, and population-based Monte Carlo. The second half of the course will involve detailed discussions of statistical models and methods for problems in genomics and computational biology, including dynamic programming, hidden Markov models, multiple sequence alignment, phylogenetic tree reconstruction, gene regulatory network discovery and analysis of genome tiling array data. Computer programming exercises would apply the methods discussed in class, primarily using the software R and BUGS/WinBUGS. During the course, students will form small groups to select a topic of interest, on which they will carry out a course project implementing statistical computing methods appropriate for the application.
[ 4 cr.] Mayetri Gupta M 230-5pm
SPH BS 853 – 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.
[ 4 cr.] Gheorghe Doros Tu 230-5pm
SPH BS 859 – Applied Genetic Analysis
Statistical tools such as linkage and association analysis are used to unravel the genetic component of complex disease. 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 linkage and association analyses, including genome-wide analyses. Special emphasis is placed on understanding assumptions and issues related to statistical methodologies for genetic analysis to identify genes influencing complex traits. Students will use specialized genetics software for homework assignments.
[ 4 cr.] Kathryn Lunetta Tu 6-845pm