BIOS 500 (3) Statistical Methods I: Fall. 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. Sample Syllabus
BIOS 500 Lab (1): Fall. 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 the statistical output for methods learned in BIOS 500 lecture. Sample Syllabus - Labs
BIOS 501 (4) Statistical Methods II: Spring. Prerequisites: BIOS 500 or permission of instructor. This course is the follow-up to Biostatistical Methods I (BIOS 500). Students will apply many of the concepts learned in BIOS 500 in a broader field of statistical analysis: model construction. Topics covered include Linear Regression, Analysis of Variance, Logistic Regression, and Survival Analysis. Students who successfully complete this course will have a deep understanding of many analytical methods used by public health researchers in daily life. BIOS 501 Lab is a component of this course. Sample Syllabus
BIOS 502 (2) Statistical Methods III: Fall. Prerequisites: BIOS 500 & BIOS 501 or permission of instructor. We start with data analytic methods not covered in BIOS 500 & BIOS 501 (Statistical Methods I & II): two-way ANOVA, polynomial regression, count regression, Kaplan-Meier analysis, multiple imputation, propensity scores. After the first exam, we focus on multilevel modeling of intra- and inter-individual change. Other hierarchical models will also be examined to analyze other types of clustered data. As in the prerequisite courses, we will learn how to specify an appropriate model so that specific research questions of interest can be addressed in a methodologically sound way. Students will use SAS to perform statistical analyses. Sample Syllabus
BIOS 505 (4) Statistics for Experimental Biology: Spring. Intended for PhD candidates in the biological and biomedical sciences. Introduces the most frequently used statistical methods in those fields, including linear regression, ANOVA, logistic regression, and nonparametric methods. Students learn the statistical skills necessary to read scientific articles in their fields, do simple analyses on their own, and be good consumers of expert statistical advice. Sample Syllabus
BIOS 506 (4) Foundations of Biostatistical Methods: Fall. Prerequisite: Multivariate Calculus (Calculus III) or permission of instructor. This course is a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. The topics include descriptive statistics; probability; detailed development of the binomial, Poisson and normal distributions; sampling distributions; point and confidence interval estimation; hypothesis testing; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data and simple linear regression. The course will also equip students with computer skills for implementing these statistical methods using standard software R. Sample Syllabus
BIOS 507 (4) Applied Regression Analysis: Spring. Prerequisites: Coursework in statistics up to and including an introduction to simple linear regression (BIOS 506 or equivalent). Familiarity with basic concepts of probability, statistical inference, and linear algebra (e.g., matrix inversion, some matrix algebra) is needed for successful completion of the course. This is the first regression analysis course in applied statistics designed for MPH students. Both theoretical and applied aspects of linear regression and generalized linear regression modeling will be covered in this course. The emphasis will be on applications. The first part of the course covers the following topics: simple linear regression, multiple linear regression, analysis of variance, confounding and interaction, residual and influence diagnostics, variable transformations, multicollinearity, model selection and validation. The second part of the course includes: generalized linear models, introduction to maximum likelihood estimation, logistic regression, nominal and ordinal logistic regression, Poisson regression. Parameter interpretation and scientific interpretation of results will be emphasized throughout the course. Students are expected to use SAS (or R), when necessary, for homework assignments. Sample Syllabus
BIOS 508 (4) Biostatistical Methods: Fall. Prerequisites: Multivariate Calculus (Calculus III) and Linear Algebra. This course provides a mathematically sophisticated introduction to the concepts and methods of biostatistical data analysis. It aims to provide the students with the skills to collaborate with investigators and statistical colleagues in the analysis of data from biomedical and public health studies and to communicate the results of statistical analyses to a broad audience. The topics include descriptive statistics; probability; detailed development of the binomial, Poisson and normal distributions and simulation of random variables from these distributions; sampling distributions; point and confidence interval estimation; simulation studies; hypothesis testing; power analysis and sample size calculations; a variety of one- and two-sample parametric and non-parametric methods for analyzing continuous or discrete data and resampling statistics. The course will also equip students with computer skills for implementing these statistical methods using standard statistical software SAS or R. Sample Syllabus
BIOS 509 (4) Applied Linear Models: Spring. Prerequisites: BIOS 508 or permission of instructor. The course covers statistical methodology for the analysis of continuous outcome data, primarily from cross-sectional studies and designed experiments. We introduce the key matrix-based methods for estimation and inference based on the multiple linear regression model. Subsequently, topics include secondary hypothesis testing and restrictions, regression diagnostics, model selection, confounding and interaction, analysis of variance and covariance, and an introduction to random effects modeling. Students will also be introduced to logistic regression modeling for binary outcome data. Sample Syllabus
BIOS 510 (4) Introduction to Probability Theory: Fall. Prerequisites: Multivariate Calculus (Calculus III) or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and limiting distributions. Sample Syllabus
BIOS 511 (4) Introduction to Statistical Inference: Spring. Prerequisites: BIOS 510 or permission of instructor. Fundamental concepts in statistical inference will be covered including: statistical models, sampling distributions, standard errors, asymptotic normality, confidence intervals, hypothesis tests, power analysis. Common frameworks for inference will be discussed including: parametric/likelihood-based inference, the delta method, bootstrap, permutation tests, Bayesian inference. Sample Syllabus
BIOS 512 (4) Probability Theory I: Fall. Prerequisites: Multivariate Calculus (Calculus III) and Linear Algebra or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and convergence concepts. Sample Syllabus
BIOS 513 (4) Statistical Inference I: Spring. Prerequisites: BIOS 512 or permission of instructor. Introduces the theory of parameter estimation, interval estimation, and tests of hypotheses. In this course, we emphasize the classical "frequentist" (i.e., Neyman-Pearson-Wald) approach to inference. As time permits, we briefly explore alternative paradigms of inference such as neo-Fisherian, Bayesian, and statistical decision theory. Sample Syllabus
BIOS 516 (1) Introduction to Large- Scale Biomedical Data Analysis: Fall. Prerequisites: BIOS 501 or permission of instructor. 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. Sample Syllabus
BIOS 520 (2) Clinical Trials Methodology: Spring. Prerequisites: BIOS 500 or BIOS 506 or permission of instructor. This course is intended to not only provide a basic grounding in all aspects of the conduct of clinical trials from the perspective of a biostatistician but also teach students the state-of-the-art knowledge in clinical trials and help them find clinical trial related jobs in pharmaceutical companies, hospitals, oncology research institutes, etc. Topics of this course include generic drug development, new drug development, pre-clinical trial, the state-of-the-art designs for contemporary Phase I, II, and III clinical trials, protocol writing, hypothesis, methods of randomization, blinding, sample size determination, ethics, subject recruitment, data collection, quality control, monitoring outcomes and adverse events, interim analysis, data analysis, issues with data analysis, reporting, interpreting results, and current advances in clinical trials. Sample Syllabus
BIOS 521 (2) Applied Survival Analysis: Fall. Prerequisites: BIOS 506, BIOS 507, BIOS 510, and BIOS 511 or permission of instructor. This course will provide an introduction to statistical concepts and methods related to the analysis of survival data. Topics include survival functions, hazard rates, types of censoring and truncation, life table, log-rank tests, Cox regression models, and parametric regression models. The emphasis is on practical implementation of standard survival analysis methods using SAS or R and results interpretations. Sample Syllabus
BIOS 522 (2) Survival Analysis Methods: Fall. Prerequisites: BIOS 508, BIOS 509, BIOS 512, and BIOS 513 or permission of instructor. This course aims to develop basic understanding of statistical concepts and methods related to the analysis of survival data. The concepts to be introduced include survival functions, hazard rates, types of censoring and truncation. Methods of focus are life table, Kaplan-Meier and Nelson-Aalen estimates, log-rank tests, Cox regression. models, and parametric regression models. Students will learn how to implement standard survival analysis methods using SAS or R and appropriately interpret results. Examples and homework assignments based on real life data will give students the opportunity to analyze survival data and produce reports of their methods, results and conclusions. Sample Syllabus
BIOS 524 (2) Introduction to Analytic Methods for Infectious Diseases: Fall or Spring. Prerequisites: BIOS 506 and BIOS 510 or permission of instructor. 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. Sample Syllabus
BIOS 525 (2) Longitudinal and Multilevel Data Analysis: Fall. Prerequisites: BIOS 507 or permission of instructor. This course introduces students to regression techniques commonly used in analyzing longitudinal and multilevel data that are frequently encountered in biomedical and public health research. This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course focuses on data analysis and interpretation. Students will gain practical experience using R/SAS/Stata for statistical computing. Sample Syllabus
BIOS 526 (3) Modern Regression Analysis: Fall. Prerequisites: Multivariate Calculus (Calculus III), Linear Algebra, BIOS 509 and BIOS 513 or permission of instructor. This course introduces students to modern regression techniques commonly used in analyzing public health data. Specific topics include: (1) parametric and non-parametric methods for modeling non-linear relationships (e.g., splines and generalized additive models); (2) methods for modeling longitudinal and multi-level data that account for within group correlation (e.g., mixed-effect models, generalized estimating equations); (3) Bayesian methods; and (4) shrinkage methods and bias-variance tradeoffs. This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course provides a survey of advanced regression approaches with a focus on data analysis and interpretation. Students will gain an understanding of methods that will facilitate future independent and collaborative research for modern research problems. Students will gain practical experience using the R language for statistical computing. Sample Syllabus
BIOS 531 (2) SAS Programming: Fall. Prerequisites: BIOS 500 & BIOS 501 or permission of instructor. This class is designed to help students master statistical programming in SAS. Students in this class will develop programming style and skills for data manipulation, report generation, simulation and graphing. This class does not directly satisfy any competencies as defined by the Department of Biostatistics and Bioinformatics, the Rollins School of Public Health or the Council on Education for Public Health (CEPH). That being said, SAS is a primary data analysis and data management software system in use worldwide, particularly in public health settings. Students who master the skills offered in this course will have a much easier time completing the work for their thesis and will find themselves more ready for a public health career with a more analytical bent. Sample Syllabus
BIOS 532 (2) Statistical Computing: Spring. Prerequisite: BIOS 506, BIOS 510, and BIOS 531 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 534 (2) Machine Learning: Spring. Prerequisites: Multivariate Calculus (Calculus III), Linear Algebra, and Python programming. 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, homework, and projects. Sample Syllabus
BIOS 540 (2) Introduction to Bioinformatics: Spring. Prerequisites: BIOS 500, BIOS 501, or BIOS 506 or permission of instructor. 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 544 (2) Introduction to R programming for Non-BIOS students: Fall and Spring. For non-BIOS Students Only. 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. Sample Syllabus
BIOS 545 (2) R Programming for BIOS students: Spring. For BIOS Students Only. 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. Sample Syllabus
BIOS 550 (2) Sampling Applications I: Spring. Prerequisites: BIOS 500, BIOS 501 or BIOS 506 or permission of the instructor. 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. Sample Syllabus
BIOS 555 (2) High-Throughput Data Analysis using R and BioConductor: Fall. Prerequisites: BIOS 501 or equivalents and basic programming in R or permission of instructor. 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. Sample Syllabus
BIOS 560R (VC) Current Topics in Biostatistics: Fall and spring. 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: Spring. Prerequisites: BIOS 506, EPI 530, or permission of instructor. This is an introductory course for graduate students in Biostatistics, Bioinformatics, Epidemiology, Genetics, Computational Biology, and other related quantitative disciplines. The course will conduct a comprehensive survey of statistical methods for the analysis of family-and population-based genetic data, including classical linkage analysis, population-based and family‐based association analysis, genome‐wide association studies (GWAS), and analysis of next-generation sequencing data. Because this course serves as a prerequisite to BIOS 770 Advanced Statistical Genetics, the focus of the course will be on identifying statistical problems, relating genetic concepts to statistical model assumptions, introducing the latest statistical methods, and ultimately preparing students for in-depth understanding/research of statistical methodologies on analysis of genetic data. Sample Syllabus
BIOS 580 (2) Statistical Practice I: Fall. Required course for MPH and MSPH Biostatistics students. This course will cover topics dedicated to preparing students to collaborate as biostatisticians for public health and biomedical projects with non-statisticians. Covered topics will include consulting versus collaboration, ethics, nonstatistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. The students will work together in small groups to develop research questions based on an existing real life dataset and discussion with a clinical collaborator, conduct power analyses, choose the appropriate statistical methodology to analyze the research questions, then answer at least one of the questions, and present the results in both oral and written format. In addition, individually each student will complete a series of milestones that result in oral and/or written proposal for an individual capstone project to be completed in the Spring semester. Sample Syllabus
BIOS 581 (2) Statistical Practice II (Capstone): Spring. Prerequisites: BIOS 580. This is a required course for the MPH and MSPH students in the Biostatistics and Bioinformatics program in their final spring semester. The purpose of the course is to help students with their capstone project in project management, manuscript writing, and oral presentation while they conduct their project with their individual BIOS advisors. Students will review journal articles to critique study design and statistical analysis methods in a journal club format. They will learn how to write journal articles section by section through lectures and homework assignments. They will develop a manuscript based on their capstone project. At the end of the semester, each student will give an oral presentation on his/her capstone project. Each student will also make a poster on his/her capstone project. Students will receive feedback from their peers and instructors to improve their writing and presentation skills. Sample Syllabus
BIOS 590R (1) Seminar in Biostatistics: Fall and spring. Features invited speakers, departmental faculty, students, and others who discuss special topics and new research findings.
BIOS 591P (3) Biostatistical Methods II: Spring. Prerequisites: BIOS 500 or permission of instructor. For EPI students Only taken in the spring semester of their first year. The course covers fundamental concepts in applied simple and multiple linear regression analyses, one- and two-way analysis of variance and binary logistic regression. Concepts in survival analysis will also be introduced. Students will learn when and how to apply these methods. The emphasis will be on practical data analysis skills rather than statistical theory; however, wherever possible and feasible, mathematical details of regression models will be presented. In-class data analysis examples will employ SAS and R software. Homework assignments, quizzes and exams will include data analyses using SAS and R, as well as other questions designed to reinforce concepts and assess foundational competencies. Teaching assistant office hours will consist of organized review/recitation sessions, and will also include opportunities for student questions. Sample Syllabus
BIOS 595R (0) Applied Practice Experience: Spring. An Applied Practice Experience (APE) is a unique opportunity that enables students to apply practical skills and knowledge learned through coursework to a professional public health setting that complements the student's interests and career goals. The APE must be supervised by a Field Supervisor and requires approval from an APE Advisor designated by the student's academic department at RSPH.
BIOS 597R (VC) Directed Study: Fall and Spring. Provides in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.
BIOS 599R (VC) Thesis: Fall, Spring, and Summer. Master’s thesis research. Sample Syllabus
BIOS 707 (4) Advanced Linear Models: Fall. Prerequisites: BIOS 509, BIOS 513, and a course in matrix algebra. 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. Sample Syllabus
BIOS 709 (4) Generalized Linear Models: Spring. Prerequisite: BIOS 513 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. 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: Fall. Prerequisite: BIOS 512 and BIOS 513. Axioms of probability, univariate and multivariate distributions, convergence of sequences of random variables, Markov chains, random processes, martingales. Sample Syllabus
BIOS 711 (4) Statistical Inference II: Spring. Prerequisite: BIOS 710. 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. Sample Syllabus
BIOS 722 (2) Advanced Survival Analysis: Fall and Spring. Prerequisite: BIOS 512, BIOS 513, BIOS 522. 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: Fall or Spring. 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, 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. Sample Syllabus
BIOS 724 (2) Analytic Methods for Infectious Disease Interventions: Fall or Spring. Prerequisites: BIOS 513. Advanced analytic, statistical, and epidemiologic methods particular to infectious diseases including analysis of infectious disease data and evaluation of intervention. Sample Syllabus
BIOS 726 (2) Applied Multivariate Analysis: Fall or Spring. Prerequisite: BIOS 509 and BIOS 513. 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. Sample Syllabus
BIOS 731 (2) Advanced Statistical Computing: Fall. Prerequisites: BIOS 544 or BIOS 545. 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). Sample Syllabus
BIOS 732 (2) Advanced Numerical Methods: Fall or Spring. Prerequisites include BIOS 532, BIOS 710 and BIOS 711, or permission of 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: Fall or Spring. BIOS 711 or permission of instructor. Some knowledge of statistical computing will be needed to complete the final project. Examines topics in the theory of estimating functions. This course presents measures of the efficiency of estimating functions; methods to produce efficient estimating functions using orthogonal projection theory; conditional estimating functions based on partially ancillary statistics; modern methods to reduce the sensitivity of an estimating function to nuisance parameters; artificial likelihood functions to accompany estimating functions; model selection issues. Applications from biomedical studies are used to illustrate the concepts discussed in class. Sample Syllabus
BIOS 736 (2) Statistical Analysis with Missing and Mismeasured Data: Fall or Spring. Prerequisites: BIOS 513 and PhD Biostatistics student. 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. Sample Syllabus
BIOS 737 (2) Spatial Analysis of Public Health Data: Fall or Spring. Prerequisites: BIOS 508, BIOS 509, BIOS 512, BIOS 513. 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. Sample Syllabus
BIOS 738 (2) Bayesian and Empirical Bayes Methods: Fall or Soring. Prerequisite: BIOS 511 or 513. 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. Sample Syllabus
BIOS 740 (2) Machine Learning: Spring. Prerequisites: BIOS 540 or permission of instructor. 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. Sample Syllabus
BIOS 745R (1) Biostatistical Consulting: Spring. This course will cover topics dedicated to preparing doctoral students to lead biostatistical collaborations with non-statisticians in public health, biology, and medicine academic environments. Covered collaboration topics will include consulting versus collaboration, ethics, non-statistical aspects of collaboration (e.g. interpersonal communication), and negotiating expectations with clients. Covered biostatistical topics will include specific aim refinement, appropriate study design for the research question, assessment of feasibility (time and effort) of different statistical methods for the same problem, statistical review of grant proposals including power calculations, and appropriate summarization/presentation of results to non-statistical audiences. Experience is the best way to nurture the critical thinking skills necessary for excellent biostatistical collaboration. Students will be given weekly assignments to further develop skills in each of the topic areas. Assignment tasks will be drawn from completed projects the course instructors have encountered. In addition, each student, under the mentorship of the course instructors or faculty in the Department of Biostatistics and Bioinformatics will engage in a collaboration experience. Each student will collaborate with a clinical investigator and provide biostatistical support to all aspects of their project. True to real-life experiences, types of projects will vary depending on the investigator and their research question of interest. Sample Syllabus
BIOS 760R (VC) Current Topics in Biostatistics: Fall or Spring. A faculty member offers a new course on a current topic of interest for PhD students.
BIOS 761 Causal Inference: Spring. This course provides a survey of modern topics in causal inference. Fundamental concepts in causal inference will be covered including: counterfactual random variables, assessing identifiability of causal effects, graphical frameworks, Gcomputation, inverse probability of treatment weighting, methods for efficient, doubly (multiply) robust estimation of causal effects, and causal mediation. Where possible, the course emphasizes the use of modern regression (e.g., machine learning) in causal effect estimation and provides an applied introduction to this area is provided as well. This course is cross-listed with EPI 760. Sample Syllabus
BIOS 770 (2) Advanced Statistical Genetics: Spring. Prerequisites: BIOS 570. 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. Sample Syllabus
BIOS 777 (1) How to Teach Biostatistics: Fall. BIOS PhD Students Only. 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) Research Methods in Biostatistics: Spring. The goals of this course are 1) to provide practical skills and knowledge to complete a PhD dissertation in biostatistics and 2) to introduce students to the research of BIOS faculty. Students will become familiar with the process of PhD research in biostatistical methods. Several topics will be covered including reading academic articles, writing tools and techniques, presentation skills, professional ethics, conducting collaborative research, and high performance computing. Lectures will include presentations by faculty giving an overview of their research with the aim of helping students choose a dissertation advisor and research area. Sample Syllabus
BIOS 795R (VC) Pre-Candidacy Research: Fall, Spring, or Summer. BIOS PhD Students Only. Research pertaining to a dissertation and preparing for the proposal.
BIOS 797R (VC) Directed Study: Fall, Spring, or Summer. 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: Fall, Spring or Summer. BIOS PhD Students Only. Research pertaining to a dissertation and preparing for the defense.
Page Last Updated: 07/01/21