Biostatistics Courses

BIOS 500 (3) Statistical Methods I: Prerequisite: Algebra. Introduces parametric and nonparametric statistical methodology, including descriptive measures, elementary probability, estimation, hypothesis testing, confidence intervals, common nonparametric methods, and base contingency table analysis. Empirically demonstrates underlying theory. (This course is for informatics and non-bios major students. If does not fulfill any requirements for a biostatistics major student.)

Sample Syllabus

BIOS 500 Lab (1): Prerequisites: Concurrent enrollment in BIOS 500. This lab complements the Bios 500 courses by using hands-on demonstrations of statistical concepts and methods taught in lecture. The statistical software, SAS, will be introduced as a programming tools to accomplish many of these tasks. Sample Syllabus - Labs

BIOS 501 (3) Statistical Methods II: Prerequisite: BIOS 500 or equivalent. Addresses estimation and hypothesis testing within the context of the general linear model. Examines in depth the analysis of variance, multiple regression, and logistic regression. Previews select advanced techniques. (The course does not fulfill core or elective requirements for biostatistics students.) Sample Syllabus

BIOS 501 Lab (1): Prerequisites: BIOS 500 and BIOS 500 Lab, and concurrent enrollment in BIOS 501. A continuation of the BIOS 500 Lab. Students learn SAS programming for the statistical methods covered in BIOS 501. Sample Syllabus - Labs

BIOS 502 (2) Statistical Methods III: Prerequisites: BIOS 500 and BIOS 501. This course introduces students to data analytic methods not covered in BIOS 500 & BIOS 501. It is focused on multilevel models, particularly modeling longitudinal data. Other hierarchical models will also be introduced to analyze other types of clustered data. Students will learn how to specify an appropriate statistical model so that specific research questions of interest can be addressed in a methodologically sound way. Sample Syllabus

BIOS 505 (4) Statistics for Experimental Biology: This course concentrates on the design and analysis of experiments, with the goal of equipping practicing scientists with the tools to analyze research data. Considerable emphasis will be placed on the application of statistical design and analysis for decision-making. Students successfully completing this course should be able to: understand and implement good experimental design in conducting scientific research, choose and carry out appropriate statistical analyses for a variety of data types, provide sound interpretation of statistical analyses, and critically read and interpret the statistical content of scientific journal articles in the biological and biomedical sciences. Sample Syllabus

BIOS 506 (4) Biostatistical Methods I: Prerequisite: matrix algebra. For biostatistics majors. Focuses on 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. Draws examples from biomedical literature. Real data set analysis is done, using statistical computer packages. Sample Syllabus

BIOS 507 (4) Applied Linear Models: Prerequisites: Biostatistics major, BIOS 506 or equivalent; one year of calculus, linear algebra, and matrix algebra. Provides 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 multifactor 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. Discusses design-related analysis issues. Demonstrates appropriate programs such as SAS and S-Plus. Sample Syllabus

BIOS 508 (2) Introduction to Categorical Data Analysis: Prerequisites: BIOS 506 and one year of calculus. This course will introduce the students to categorical data analysis. It will cover topics such as distributions, goodness of fit, contingency tables (traditional approach), logistic models for contingency tables, logistic regression, logistic models for multi-category data, poison regression, and matched paired data. Sample Syllabus

BIOS 510 (4) Introduction to Probability Theory: Prerequisite: calculus and multivariate analysis. Focuses on 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. Sample Syllabus

BIOS 511 (4) Statistical Inference I: Prerequisite: BIOS 510. Focuses on sampling distributions, parametric point and interval estimation, tests of hypotheses, decisions theory, and Bayesian inference. Sample Syllabus

BIOS 512 (4) Probability Theory I: Prerequisite: calculus and multivariate analysis. Introduction to probability, random Vaiables, distributions, conditional distributions, expectations, moment generating functions, and convergence concepts.

BIOS 520 (2) Clinical Trials Methodology: Prerequisite: BIOS 500, BIOS 504, or BIOS 506. Covers the organization, methodology, and reporting results of clinical trials. Topics covered include conceptualization, data collection, ethical considerations, and protocol adherence and compliance, as well as statistical techniques such as randomization, double-blind techniques, sample size determination, and analysis considerations. Sample Syllabus

BIOS 522 (2) Survival Analysis Methods: Prerequisites: BIOS 500 and BIOS 501, or BIOS 506 and BIOS 706. Deals with the modern methods used to analyze time-to-event data. Provides background theory, but emphasis is on using methods and interpreting results. 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. Sample Syllabus

BIOS 524 (2) Introduction to Analytic Methods for Infectious Diseases:Prerequisites: BIOS 506 and BIOS 510 or equivalent. Introduces dynamic and epidemiological concepts particular to infectious diseases, including elements of the infection process; transmission patterns; epidemic, endemic, micro- and macroparasitic diseases; zoonoses; basic reproduction number; dependent happenings; and effects of intervention. Sample Syllabus

BIOS 526 (3) Modern Regression Analysis: Prerequisites: BIOS 507 or permission from the instructor. Students should be familiar with matrix notations, multiple regression, and basic probability. 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. Sample Syllabus

BIOS 531 (2) SAS Programming: Prerequisites: BIOS 501 or equivalent, OR BIOS 506 (concurrent), OR permission of the instructor. 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 final exam for the course is the Base SAS Certification exam. Students who pass this exam successfully receive a certificate of completion from the SAS Institute. Sample Syllabus

BIOS 532 (2) Statistical Computing: Prerequisite: BIOS 531, BIOS 506, and BIOS 510, or permission of instructor. 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. Sample Syllabus

BIOS 536 (2) Modern Nonparametrics and Regression Methods: Prerequisites: BIOS 501 or BIOS 706 and BIOS 511. Focuses on robust estimates, jackknife, bootstrap, cross-validation, smoothing methods, generalized additive models, classification, and regression trees. Study of many different applications is included. Strong computing background is required. 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. Prerequisites: Bios 506 and Bios 510 or permission of instructor. Sample Syllabus

BIOS 550 (2) Sampling Applications: Prerequisite: BIOS 501 or BIOS 506. Focuses on how to select probability samples and analyze the data, using simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. The software package PC-SUDAAN is used for data analysis. Sample Syllabus

BIOS 551 (2) Sampling Theory: Prerequisite: BIOS 550. Examines the theoretical justification for the applications covered in BIOS 550.

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.
Sample Syllabus

BIOS 590R (1) Seminar in Biostatistics: Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)

BIOS 595R (0) Practicum Enables students to apply skills and knowledge 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: Prerequisites: BIOS 507, BIOS 511, and a course in matrix algebra. Focuses on 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.

BIOS 709 (4) Generalized Linear Models: Prerequisites: BIOS 511 and BIOS 707. 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. Studies methods for examining model assumptions. Introduces generalized estimating equations (GEE) and quadratic estimating equations 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. Sample Syllabus

BIOS 710 (4) Probability Theory II: Prerequisites: BIOS 510 and BIOS 511. Focuses on axioms of probability, univariate and multivariate distributions, convergence of sequences of random variables, Markov chains, random processes, and martingales. Sample Syllabus

BIOS 711 (4) Statistical Inference II: Prerequisite: BIOS 710. Examines the fundamental role of the likelihood function in statistical inference, ancillary and sufficient statistics, estimating functions, and asymptotic theory. 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. Briefly discusses alternative approaches to inference including Bayesian, Likelihood Principle, and decision theory. Sample Syllabus

BIOS 722 (2) Advanced Survival Analysis: Prerequisites: BIOS 510, BIOS 511, and BIOS 706. Provides 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. Sample Syllabus

BIOS 723 (4) Stochastic Processes: Prerequisites: matrix algebra and BIOS 710. Provides dual coverage of the theory and methods for dealing with the diversity of problems involving branching processes, random walks, Poisson processes, birth and death processes, Gibbs sampling, martingale counting processes, hidden Markov chains, inference on semi-Markov chains, and chain of events modeling. Draws applications from the biological sciences, including the theory of epidemics, genetics, survival analysis, models of birth-migration-death, and the design and analysis of HIV vaccine trials.

BIOS 724 (2) Analytic Methods for Infectious Disease Interventions: Prerequisite: BIOS 511. Focuses on advanced analytic, statistical, and epidemiological methods particular to infectious diseases, including analysis of infectious disease data and evaluation of intervention.

BIOS 726 (2) Applied Multivariate Analysis: Prerequisites: BIOS 511. Investigates multivariate techniques. Main subjects 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. Demonstrates programs such as SAS and S-PLUS. Sample Syllabus

BIOS 732 (2) Advanced Numerical Methods: Prerequisites include BIOS 532, BIOS 710 and BIOS 711, or permission of the instructor. BIOS 711 may be taken concurrently.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. Sample Syllabus

BIOS 735 (2) Estimating Function Theory: Prerequisites: BIOS 711 or permission of instructor; some knowledge of statistical computing will be needed to complete the final project. Estimating function theory provides a framework that unifies many seemingly unrelated approaches to estimation, including maximum likelihood, quasi-likelihood, and minimum variance unbiased estimation. This course examines topics in the theory of estimating functions that are especially relevant in biostatistics. A distinctive feature of this course is its emphasis on orthogonal projection theory as a general technique to generate optimal estimating functions. Applications from biomedical studies are used to illustrate the concepts. Sample Syllabus

BIOS 736 (2) Statistical Analysis with Missing and Mismeasured Data: Prerequisites: BIOS 511 and knowledge of S-plus. For PhD biostatistics students; others must obtain permission of instructor. Introduces concepts and methods of analysis for missing data. Topics include methods for distinguishing ignorable and nonignorable missing data mechanisms, single and multiple imputation, and hot-deck imputation. Computer-intensive methods are used.

BIOS 737 (2) Spatial Analysis of Public Health Data: Prerequisites: BIOS 506, 507, 510, 511. Familiarizes students with statistical methods and underlying theory for the spatial analysis of georeferenced public health data. Topics covered include kriging and spatial point processes. Includes a review of recent computational advances for applying these methods.

BIOS 738 (2) Bayesian and Empirical Bayes Methods: Prerequisites: BIOS 510 and BIOS 511. 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.

BIOS 739 (2) Longitudinal Data Analysis: Prerequisite: BIOS 510 and BIOS 511. Focuses on design considerations, exploratory data analysis, general linear models, parametric models for covariance structure, generalized linear models, analysis of variance, transition models, and missing values.

BIOS 740 (2): Bioinformatic Machine Learning. This course covers some popular supervised and unsupervised machine learning techniques in Bioinformatics and general high-dimensional data research. The topics covered fall into three categories – classification, clustering and dimension reduction. Prerequisites: BIOS 540 or permission of instructor. Sample Syllabus

BIOS 745R (1) Biostatistical Consulting: Prerequisite: BIOS 507. Focuses on the roles, responsibilities, and other issues related to the biostatistician as consultant or collaborator in the biomedical field. Initially focuses on preparing students to act as consultants through discussions of consulting models, interpersonal communication, ethics, common client types, time and financial management, and other issues. Students then collaborate with researchers to develop the design and/or the analysis of quantitative investigations, initially under supervision of a faculty member and later independently. This collaboration is reviewed and critiqued by faculty and students. May be taken more than once for credit, but not as fulfillment of biostatistics elective. Sample Syllabus

BIOS 760R (VC) Advanced Topics in Biostatistics: A faculty member offers a new course on an advanced topic of interest, such as spatial analysis, time series, missing data methods, causal inference, and discrete multivariate analysis.
Sample Syllabus - Advanced Bayesian Modeling Sample Syllabus - Quantile Regression

BIOS 777 (1) How to Teach Biostatistics: This course 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 780R (1) Advanced PhD Seminar: Prerequisite: BIOS 511. Acquaints students with a variety of areas of biostatistical research and provides the chance to do preliminary reading in an area of interest. Each student reads a few papers in an area of interest, and presents the material to the group. Topics and readings can be suggested by the faculty member in charge or by the students. This course may be repeated for credit. (Satisfactory/unsatisfactory grading only).

BIOS 790R (1) Advanced Seminar in Biostatistics: Invited speakers, faculty, and advanced students discuss special topics and new research findings. (Satisfactory/unsatisfactory grading only.)

BIOS 797R (VC) Directed Study: Provides in-depth exposure to advanced special topics not covered in regular courses.

BIOS 798R (VC) Special Projects: Involves intern-like participation at advanced levels on specific scholarly, research, or developmental projects. Students assume independent roles as statistical consultants and collaborators in a variety of research settings.

BIOS 799R (VC) Thesis: Dissertation research.

Current Semester's Course Schedules and Relevant RSPH Information

Public Health Informatics Course Descriptions and Syllabi
Page Updated 12/5/12