BIOS 500 (3) Statistical Methods I: This course is designed to teach students the fundamentals of applied statistical data analysis. Students successfully completing this course will be able to: choose appropriate statistical analyses for a variety of data types; perform exploratory data analyses; implement commonly used one and two-sample hypothesis testing and confidence interval methods for continuous variables; perform tests of association for categorical variables; conduct correlation and simple linear regression analyses; produce meaningful reports of statistical analyses and provide sound interpretations of analysis results. Students will be able to implement the statistical methods learned using SAS and JMP software on personal computers.
BIOS 500 (1) Statistical Methods I Lab: The lab portion of BIOS 500 is designed with two purposes in mind: 1) to illustrate concepts and methods presented in the lectures using hands-on demonstrations and 2) to introduce SAS, a widely used statistical software package, as a data analysis tool. By the end of the semester, you should be able to produce and interpret statistical output for methods learned in BIOS 500 lecture.
Sample Syllabus - Labs
BIOS 501 (3) Statistical Methods II: This course is a follow-up to Statistical Methods I, and assumes a certain degree of comfort with these materials. It is composed of three distinct units: (1) covariance, correlation, and simple linear regression, (2) multiple linear regression and model building, and (3) ANOVA, logistic regression and survival analysis. To instill you with confidence in these topic areas, learning will take place through a variety of tasks and assignments administered at frequent intervals, including (but not limited to): problem sets, quizzes, readings, and lab assignments (graded separately). I will use these to evaluate your overall performance, in conjunction with three unit exams and a cumulative final exam. Sample Syllabus
BIOS 501 (1) Statistical Methods II Lab: Students in the lab component for BIOS 501 will apply the statistical methods covered in the lecture directly to real datasets using SAS. Modeling strategies and data management / SAS programming will be covered in more depth in this session, with particular focus on modeling methods and preparing data for SAS modeling procedures (indicator variables, data cleaning, outlier detection, etc.). Sample Syllabus - Labs
BIOS 502 (2) Statistical Methods III: This course introduces students to data analytic methods not covered in the BIOS 500 & BIOS 501 (Statistical Methods I & II). It is focused on multilevel models, particularly modeling longitudinal data. Issues involved with the analysis of repeated measures data, particularly missing data, are also covered. Sample Syllabus
BIOS 506 (4) Biostatistical Methods I: Mathematically sophisticated presentations of principles and methods of data description; exploratory data analysis; graphics; point and confidence interval estimation; hypothesis testing; relative risk; odds ratio; Mantel-Haenszel test; chi-square tests; simple linear regression; correlation; and one- and two-sample parametric and nonparametric tests. Examples will be drawn from biomedical literature; real data set analysis will be done, using statistical computer packages. Concurrent registration in BIOS 531 recommended. Prerequisite: Matrix algebra; Biostatistics major. Sample Syllabus
BIOS 507 (4) Applied Linear Models: This course will provide sound statistical methods for the analyses of continuous data from observational studies and designed experiments. The analyses methods include multiple linear regression with model building (selection of predictor variables, diagnostics, residual analysis, collinearity, and simultaneous inferences); one-way, two-way and multi-factor analysis of variance (both balanced and unbalanced studies); analysis of covariance; fixed effect, random effect and mixed effect models; mathematically sophisticated introduction to linear models in matrix form. Study designs include sample size planning, randomized block designs, nested designs, repeated measures designs, split-plot designs, and Latin squares designs. Design related analysis issues will be discussed. Appropriate programs such as SAS and S-Plus will be demonstrated. Prerequisite: Biostatistics major, BIOS 506 or equivalent; one year of calculus, linear algebra, and matrix algebra. Sample Syllabus
BIOS 508 (2) Introduction to Categorical Data Analysis: This course will introduce the students to categorical data analysis. It will cover topics such as: discrete distributions, goodness of fit; contingency tables (traditional approach); logistic models for contingency tables; logistic regression; logistic models for multi-category data; Poisson regression; and matched paired data. Prerequisites: BIOS 506, BIOS 507, BIOS 510 or BIOS 512 Sample Syllabus
BIOS 510 (4) Probability Theory I: Axiomatic probability, random variables, distribution theory, special parametric families of univariate distributions, joint and conditional distributions, distributions of functions of random variables, and probability modeling. Prerequisite: calculus and multivariate analysis. Sample Syllabus
BIOS 511 (4) Statistical Inference I: Sampling distributions, parametric point and interval estimation, tests of hypotheses, decision theory, and Bayesian inference. Prerequisite: BIOS 510. Sample Syllabus
BIOS 512 (4) Probability Theory I: Introduction to probability, random variables, distributions, conditional distributions, expectations, moment generating functions, and convergence concepts.
BIOS 516 (1) Introduction to Large- Scale Biomedical Data Analysis: This is the overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics. This course will be co-taught by all BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and hot areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.
BIOS 520 (2) Clinical Trials Methodology: Covers the organization, methodology, and reporting results of clinical trials. Topics covered include: conceptualization, ethical considerations, protocol adherence and compliance, and data collection, as well as statistical techniques such as randomization, double-blind techniques, sample size determination, and analysis considerations. Prerequisites: BIOS 500, or BIOS 506. Sample Syllabus
BIOS 522 (2) Survival Analysis Methods: Deals with the modern methods used to analyze time-to-event data. Background theory is provided, but the emphasis is on using methods and interpreting the results. The course provides coverage of survivorship functions, Kaplan-Meier curves, logrank test, Cox regression, model fitting strategies, model interpretation, stratification, time dependent covariates, and introduction to parametric survival models. Computer programs are used. A data analysis project is required. Prerequisites: BIOS 506, BIOS 507, BIOS 510 or BIOS 512. Sample Syllabus
BIOS 524 (2) Introduction to Analytic Methods for Infectious Diseases: Introduces dynamic and epidemiologic concepts particular to infectious diseases, including the elements of the infection process; transmission patterns, epidemic, endemic, micro- and macroparasitic diseases; zoonoses, basic reproduction number; dependent happenings; and direct and indirect effect of intervention. Prerequisites: Previous or concurrent enrollment in BIOS 500 or BIOS 506, and BIOS 510. Sample Syllabus
BIOS 526 (3) Modern Regression Analysis: This course introduces students to modern regression techniques commonly used in analyzing public health data. Topics include: (1) parametric and non-parametric methods for modeling non-linear relationships; (2) methods for modeling longitudinal and multilevel data that account for within group correlation; (3) Bayesian regression modeling; and (4) methods for multivariate outcomes. Prerequisites: BIOS 507 or instructor’s permission. Sample Syllabus
BIOS 531 (2) SAS Programming: This course offers instruction in basic SAS programming. It assumes no prior knowledge of SAS, and begins with an introduction to the data step and procedure call. Topics covered include: dataset manipulation, report writing, arrays, looping, simulation, SAS macro, SAS Interactive Matrix Language (IML), SAS Graphics, and SAS Output Delivery System (ODS). The course prepares students to take the Base SAS Certification exam. Students who pass this exam successfully receive a certificate of completion from the SAS Institute. Prerequisites: BIOS 501 or equivalent, OR BIOS 506 (concurrent), OR permission of the instructor. Sample Syllabus
BIOS 532 (2) Statistical Computing: Programming style and efficiency, data management and data structures, hardware and software, maximum likelihood estimation, matrix methods and least squares, Monte Carlo simulation, pseudo-random number generation, bootstrap, and UNIX-based computing and graphical methods. Prerequisite: BIOS 531, BIOS 506, and BIOS 510, or permission of instructor. Sample Syllabus
BIOS 540 (2) Introduction to Bioinformatics: This course is an introduction to the field of Bioinformatics for students with a quantitative background. The course covers biological sequence analysis, introductions to genomics, transcriptomics, proteomics and metabolomics, as well as some basic data analysis methods associated with the high-throughput data. In addition, the course introduces concepts such as curse of dimensionality, multiple testing and false discovery rate, and basic concepts of networks. Sample Syllabus
BIOS 534 (2) Machine Learning: This course covers fundamental machine learning theory and techniques. The topics include basic theory, classification methods, model generalization, clustering, and dimension reduction. The material will be conveyed by a series of lectures, homeworks, and projects.
BIOS 550 (2) Sampling Applications I: How to select probability samples and analyze data using simple random sampling, stratified random sampling, cluster sampling and multistage sampling. The software package PC-SUDAAN is used for data analysis. Prerequisite: BIOS 500 and 501 or BIOS 506. Sample Syllabus
BIOS 544 (2) Introduction to R programming for Non-BIOS students: The goal of the course is to will provide an introduction to R in organizing, analyzing, and visualizing data. Once you've completed this course you'll be able to enter, save, retrieve, summarize, display and analyze data.
BIOS 545 (2) R Programming for BIOS students: This course covers the basic contents of R programming with applications on statistical data analysis. Topics include data types, language syntax, basic graphics, debugging, creating packages and documentation.
BIOS 555 (2) High-Throughput Data Analysis using R and BioConductor: This course covers the basics of microarray and second-generation sequencing data analysis using R/BioConductor and other open source software. Topics include gene expression microarray, RNA-seq, ChIP-seq and general DNA sequence analyses. We will introduce technologies, data characteristics, statistical challenges, existing methods and potential research topics. Students will also learn to use proper Bioconductor packages and other open source software to analyze different types of data and deliver biologically interpretable results.
BIOS 560R (VC) Current Topics in Biostatistics: A faculty member offers a new course on a current topic of interest for both PhD and Master's students.
BIOS 570 (2) Introduction to Statistical Genetics: This is an introductory course for graduate students in Biostatistics, Bioinformatics, Epidemiology, Genetics, Computational Biology, and other related quantitative disciplines. The course will cover statistical methods for the analysis of family and population based genetic data. Topics covered will include classical linkage analysis, population-based and family‐based association analysis, haplotype analysis, genome‐wide association studies, basic principles in population genetics, imputation-based analysis, pathway‐based analysis, admixture mapping, analysis of copy number variations, and analysis of massively parallel sequencing data. Students will be exposed to the latest statistical methodology and computational tools on gene mapping for complex human diseases.
BIOS 590R (1) Seminar in Biostatistics: Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings.
BIOS 595R (0) Practicum Enables students to apply skills and knowledge in an applied setting through a supervised field training experience in a public health setting that complements the student’s interests and career goals. Must meet RSPH guidelines and have departmental approval.
BIOS 597R (VC) Directed Study: Provides an in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.
BIOS 598R (VC) Special Projects: Involves intern-like participation on specific scholarly, research, or developmental projects that expose students to the role of the statistical consultant or collaborator in a variety of research settings.
BIOS 599R (VC) Thesis: Master's thesis research.
BIOS 707 (4) Advanced Linear Models: Generalized inverse of a matrix; vectors of random variables; multivariate normal distribution; distribution theory for quadratic forms of normal random variable; fitting the general linear models by least squares; design matrix of less than full rank; estimation with linear restrictions; estimable functions; hypothesis testing in linear regression; and simultaneous interval estimation. Prerequisites: BIOS 507, BIOS 511, and a course in matrix algebra.
BIOS 709 (4) Generalized Linear Models: Studies analysis of data using generalized linear models, as well as models with generalized variance structure. Parametric models include exponential families such as normal, binomial, Poisson, and gamma. Iterative reweighted least squares and quasi-likelihood methods are used for estimation of parameters. Methods for examining model assumptions are studied. Generalized estimating equations (GEE) and quadratic estimating equations are introduced for problems where no distributional assumptions are made about the errors except for the structure of the first two moments. Illustrations with data from various basic science, medicine, and public health settings. Prerequisite: BIOS 511 and BIOS 707. Sample Syllabus
BIOS 710 (4) Probability Theory II: Axioms of probability, univariate and multivariate distributions, convergence of sequences of random variables, Markov chains, random processes, martingales. Prerequisite: BIOS 512 and BIOS 511. Sample Syllabus
BIOS 711 (4) Statistical Inference II: Examines the fundamental role of the likelihood function in statistical inference, ancillary and sufficient statistics, estimating functions, and asymptotic theory. This course presents conditional, profile and other approximate likelihoods; various ancillary concepts; generalizations of Fisher information in the presence of nuisance parameters; optimality results for estimating functions; and consistency/asymptotic normality of maximum likelihood and estimation function-based estimators. It briefly discusses alternative approaches to inference including Bayesian, Likelihood Principle, and decision theory. Prerequisite: BIOS 710. Sample Syllabus
BIOS 722 (2) Advanced Survival Analysis: In-depth coverage of theory and methods of survival analysis, including censoring patterns and theory of competing risks, nonparametric inference, estimating cumulative hazard functions, Nelson estimator, parametric models and likelihood methods, special distributions, two sample nonparametric tests for censored data, power considerations and optimal weights, sample size calculations for design purposes, proportional hazards model, partial likelihood, parameter estimation with censored data, time-dependent covariates, stratified Cox model, accelerated failure time regression models, grouped survival analysis, multivariate survival analysis, and frailty models. Prerequisite: BIOS 510, BIOS 511, BIOS 522. Sample Syllabus
BIOS 723 (4) Stochastic Processes: Provides dual coverage of the theory and methods for dealing with the diversity of problems involving branching processes, random walks, Poisson processes, and birth and death processes, Gibbs sampling, martingale counting processes, hidden Markov chains, inference on semi-Markov chains and chain of events modeling. Applications will be drawn from the biological sciences, including the theory of epidemics, genetics, survival analysis, and models of birth-migration-death, and the design and analysis of HIV vaccine trials. Prerequisites: Matrix algebra and BIOS 710.
BIOS 724 (2) Analytic Methods for Infectious Disease Interventions: Advanced analytic, statistical, and epidemiologic methods particular to infectious diseases including analysis of infectious disease data and evaluation of intervention. Prerequisites: BIOS 511.
BIOS 726 (2) Applied Multivariate Analysis: This course investigates multivariate techniques. The main subject areas covered are inferences about multivariate means, multivariate regression, multivariate analysis of variance (MANOVA) and covariance (MACOVA), principal components, factor analysis, discriminant analysis and classification, and cluster analysis. Appropriate programs such as SAS and S-PLUS will be demonstrated. Prerequisite: BIOS 507 and BIOS 511. Sample Syllabus
BIOS 731 (2) Advanced Statistical Computing: This course covers the theories and applications of some common statistical computing methods. Topics include Markov chain Monte Carlo (MCMC), hidden Markov model (HMM), Expectation-Maximization (EM) and Minorization-Maximization (MM), and optimization algorithms such as linear and quadratic programming. The class has two main goals for students: (1) learn the general theory and algorithmic procedures of some widely used statistical models; (2) develop fluency in statistical programming skills. The class puts more emphasis on implementation instead of statistical theories. Students will gain computational skills and practical experiences on simulations and statistical modeling. This course requires significant amount of programming. Each set of homework involves the implementation of certain algorithms using high-level programming language (such as Matlab or R).
BIOS 732 (2) Advanced Numerical Methods: The course covers topics in traditional numerical analysis specifically relevant to statistical estimation and inference. The topics covered include numerical linear algebra, the root finding problem (maximum likelihood) methods such as IRLS, Newton-Raphson, and EM algorithm, and Bayesian techniques for marginalization and sampling for use in statistical inference (MCMC methods). Additional topics may include numerical integration and curve fitting. Prerequisites include BIOS 532, BIOS 710 and BIOS 711, or permission of the instructor. BIOS 711 may be taken concurrently. Sample Syllabus
BIOS 735 (2) Estimating Function Theory: Examines topics in the theory of estimating functions. This course presents measures of efficiency of estimating functions; methods to produce efficient estimating functions using orthogonal projection theory; modern methods to reduce the sensitivity of an estimating function to nuisance parameters; artificial likelihood functions to accompany estimating functions; and model selection issues. Applications from biomedical studies are used to illustrate the concepts discussed in class. Prerequisites: BIOS 711 or permission of instructor; some knowledge of statistical computing will be needed to complete the final project. Sample Syllabus
BIOS 736 (2) Statistical Analysis with Missing and Mismeasured Data: The goal of the course is to introduce the concepts and methods of analysis for missing data. Topics will include methods for distinguishing ignorable and non-ignorable missing data mechanisms, single and multiple imputation, hot-deck imputation. Computer intensive methods will be used. Prerequisites: BIOS 511 and PhD Biostatistics student.
BIOS 737 (2) Spatial Analysis of Public Health Data: This course will familiarize students with statistical methods and underlying theory for the spatial analysis of georeferenced public health data. Topics covered include kriging and spatial point processes. In addition, review recent computational advances for applying these methods. Prerequisites: BIOS 506, BIOS 507, BIOS 510, BIOS 511.
BIOS 738 (2) Bayesian and Empirical Bayes Methods: Includes Bayesian approaches to statistical inference, point and interval estimation using Bayesian and empirical Bayesian methods, representation of beliefs, estimation of the prior distribution, robustness to choice of priors, conjugate analysis, reference analysis, comparison with alternative methods of inference, computational approaches, including Laplace approximation, iterative quadrature, importance sampling, and Markov Chain Monte Carlo (Gibbs sampling). Various applications such as small area estimation, clinical trials and other biomedical applications will be used as examples. Prerequisite: BIOS 511.
BIOS 745R (1) Biostatistical Consulting: The first part of the course is dedicated to preparing students to act as consultants through discussions of consulting models, interpersonal communication, ethics, common client types, time and financial management and other issues. This course is also designed to give students some practical experience as a biostatistical consultant. Students will meet with clients, analyze data sets and produce summary reports. Sample Syllabus
BIOS 760R (VC) Current Topics in Biostatistics: A faculty member offers a new course on a current topic of interest for PhD students. Sample Syllabus - Advanced Bayesian Modeling
Sample Syllabus - Quantile Regression
BIOS 770 (2) Advanced Statistical Genetics: This course provides a comprehensive survey of the statistical methods that have been recently developed for the designs and analysis of genetic association studies. Specific topics include genome-wide association studies, likelihood inference and EM algorithm, case-control sampling and retrospective likelihood, secondary phenotypes in case-control studies, haplotypes and untyped SNPs, population stratification, meta-analysis, multiple testing, winner’s curse, copy number variants, next-generation sequencing studies, rare variants and trait-dependent sampling.
BIOS 777 (1) How to Teach Biostatistics: Prepares students for teaching introductory level courses in biostatistics. The topics discussed are: syllabus development, lecturing, encouraging and managing class discussion, evaluating student performance, test and examinations, cheating, the role of the teaching assistant, teacher-student relationships, teaching students with weak quantitative skills, teaching students with diverse backgrounds, teaching health sciences students, teaching medical students, use of audio-visual techniques, and use of computers. Each student is required to teach a certain subject to the other students and the instructor, followed by a discussion of presentation strengths and weaknesses. Sample Syllabus
BIOS 795R (VC) Pre-Candidacy Research: Research pertaining to a dissertation and preparing for the proposal.
BIOS 797R (VC) Directed Study: Provides an in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.
BIOS 799R (VC) Dissertation: Research pertaining to a dissertation and preparing for the defense.
Page Last Updated: 04/26/17