Three views of courses offered by faculty in Biostatistics (BIOS):
The following courses require special arrangements with instructor for both all semesters offered:
BIOS 740, 842,850, 992, 993 and 994
These are the official descriptions taken from the University catalog. Additional courses may be added on a semester basis at the discretion of the department.
500H/540H INTRODUCTION TO BIOSTATISTICS (3). Prerequisites, Math 231 and 232. Co-requisite BIOS 511 recommended. Access to SAS software and MS Excel is required. A previous course in statistics (such as AP Statistics or STOR 151) is helpful but not required. Permission of the instructor is required for non-majors. Bios 600 is an introductory course in probability, data analysis, and statistical inference designed for the background of BSPH Biostatistics students. Topics include sampling design, descriptive statistics, probability, confidence intervals, tests of hypotheses, chi-square distribution, sets of 2-way tables, power, sample size, ANOVA, non-parametric tests, correlation, linear regression, and survival analysis. Fall.
600 PRINCIPLES OF STATISTICAL INFERENCE (3). Prerequisite, knowledge of basic descriptive statistics. Major topics include elementary probability theory, probability distributions, estimation, tests of hypotheses, chi-squared procedures, regression, and correlation. Fall and Spring.
610 BIOSTATISTICS FOR LABORATORY SCIENTISTS (3). Prerequisite, elementary calculus. Introduces the basic concepts and methods of statistics, focusing on applications in the experimental biological sciences. Spring.
511 INTRODUCTION TO STATISTICAL COMPUTING AND DATA MANAGEMENT (4). Prerequisite, previous or concurrent course in applied statistics or permission of the instructor. Introduction to use of computers to process and analyze data, concepts and techniques of research data management, and use of statistical programming packages and interpretation. Focus is on the use of SAS for data management, with an introduction to use of SAS for reporting and analysis. Fall.
540 PROBLEMS IN BIOSTATISTICS (1 or more). Prerequisites to be arranged with the faculty in each case. A course for students of public health who wish to make a study of some special problem in the statistics of the life sciences and public health. Fall, spring, and summer.
541 QUANTITATIVE METHODS FOR HEALTH CARE PROFESSIONALS I (4). Prerequisite, permission of instructor. Course is designed to meet the needs of health care professionals who need to be able to critically appraise the design and analysis of medical and health care studies and intend to pursue academic research careers. Basics of statistical inference, analysis of variance, multiple regression, categorical data analysis, and an introduction to logistic regression and survival analysis. Emphasis is on applied data analysis of major health care studies. Fall.
545 PRINCIPLES OF EXPERIMENTAL ANALYSIS (3). Prerequisites, BIOS 600 or equivalent; a basic familiarity with a statistical software package (preferably SAS) that has the capacity to do multiple linear regression analysis; permission of the instructor except for majors in School of Public Health. Continuation of Biostatistics 600; the analysis of experimental and observational data, including multiple regression, and analysis of variance and covariance. Spring.
550 BASIC ELEMENTS OF PROBABILITY AND STATISTICAL INFERENCE I (GNET 636) (4). Prerequisite, MATH 232 or equivalent. Fundamentals of probability, discrete and continuous distributions; functions of random variables; descriptive statistics; fundamentals of statistical inference, including estimation and hypothesis testing. Fall.
613 DATA MANAGEMENT IN CLINICAL AND PUBLIC HEALTH RESEARCH (3). Prerequisite, familiarity with basic health research designs (e.g., BIOS 664 or 668, EPID 726 or 733, MHCH 713, INSL 780, or equivalent) or permission of the instructor. This course introduces theoretical and practical aspects of data management architecture, processes and applications in clinical and public health research. Spring.
660 PROBABILITY AND STATISTICAL INFERENCE I (3). Prerequisite, MATH 233 or equivalent. Introduction to probability; discrete and continuous random variables; expectation theory; bivariate and multivariate distribution theory; regression and correlation; linear functions of random variables; theory of sampling; introduction to estimation and hypothesis testing. Fall.
661 PROBABILITY AND STATISTICAL INFERENCE II (3). Prerequisite, BIOS 660. Distribution of functions of random variables; Helmert transformation theory; central limit theorem and other asymptotic theory; estimation theory; maximum likelihood methods; hypothesis testing; power; Neyman-Pearson Theorem, likelihood ratio, score, and Wald tests; noncentral distributions. Spring.
662 INTERMEDIATE STATISTICAL METHODS (4). Corequisites, BIOS 511, 550, or equivalents. Principles of study design, descriptive statistics, and sampling from finite and infinite populations, with particular attention to inferences about location and scale for one, two, or k sample situations. Both distribution-free and parametric approaches are considered. Gaussian, binomial, and Poisson models, one-way and two-way contingency tables, as well as related measures of association, are treated. Fall.
663 INTERMEDIATE LINEAR MODELS (4). Prerequisite, BIOS 662 or equivalent. Matrix-based treatment of regression, one-way and two-way ANOVA, and ANCOVA, emphasizing the general linear model and hypothesis, as well as diagnostics and model building. The course begins with a review of matrix algebra, and it concludes with some treatment of statistical power for the linear model and with binary response regression methods. Spring.
664 SAMPLE SURVEY METHODOLOGY (4). Prerequisite, BIOS 550 or equivalent or permission of the instructor. Fundamental principles and methods of sampling populations, with primary attention given to simple random sampling, stratified sampling, and cluster sampling. Also, the calculation of sample weights, dealing with sources of nonsampling error, and analysis of data from complex sample designs are covered. Practical experience in sampling is provided by student participation in the design, execution, and analysis of a sampling project. Spring.
665 ANALYSIS OF CATEGORICAL DATA (3). Prerequisites, BIOS 550, 662, and 663 or equivalent. Introduction to the analysis of categorized data: rates, ratios, and proportions; relative risk and odds ratio; Cochran-Mantel-Haenszel procedure; survivorship and life table methods; linear models for categorical data. Applications in demography, epidemiology, and medicine. Fall.
666 APPLIED MULTIVARIATE ANALYSIS (3). Prerequisite, BIOS 663 or equivalent. Application of multivariate techniques, with emphasis on the use of computer programs. Multivariate analysis of variance, multivariate multiple regression, weighted least squares, principal component analysis, canonical correlation and related techniques.
667 APPLIED LONGITUDINAL DATA ANALYSIS (3). Prerequisite: Students should be familiar with basic notions of probability, random variables, and statistical inference; analysis of variance; and (multiple) linear regression at the level of Bios 545 and/or Bios 663. Familiarity with matrix algebra is also useful. We will review matrix algebra at the beginning of the course and make considerable use of matrix notation and operations throughout. SAS will be used extensively in the class and hence students are expected to have had some exposure to the use of SAS. The course is meant to be accessible both to non-majors and majors. The underlying mathematical theory will not be stressed, and the main focus will be on concepts and applications. Fall.
668 DESIGN OF PUBLIC HEALTH STUDIES (3). Prerequisites, BIOS 511, 545, 550, or equivalents. Statistical concepts in basic public health study designs: cross-sectional, case-control, prospective, and experimental (including clinical trials). Validity, measurement of response, sample size determination, matching and random allocation methods. Spring.
670 DEMOGRAPHIC TECHNIQUES I (3). Source and interpretation of demographic data; rates and ratios, standardization, complete and abridged life tables; estimation and projection of fertility, mortality, migration, and population composition. Fall.
680 INTRODUCTORY SURVIVORSHIP ANALYSIS (3). Prerequisite, BIOS 661 or permission of the instructor. Introduction to concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, regression techniques, applications to clinical trials. Spring.
691 FIELD OBSERVATIONS IN BIOSTATISTICS (1). Field visits to, and evaluation of, major nonacademic biostatistical programs in the Research Triangle area. (Field fee $25.). Fall
700 RESEARCH SKILLS IN BIOSTATISTICS (1). Prerequisites, either completion of BIOS 760 and 761 (or 758), 762, 763, an d767 or successful passing grade on either doctoral qualifying examination in biostatistics. This course will introduce doctoral students in biostatistics to research skills necessary for writing a dissertation and for a career in research.
735 STATISTICAL COMPUTING - BASIC PRINCIPLES AND APPLICATIONS (3). Prerequisites, BIOS 661; familiarity with at least one computer system and with either a computer language (C, FORTRAN, etc.) or a computer package (SAS, SPSS, etc.). Basic theory and application of computing as a tool in statistical research and practice. Topics include: algorithms and data structures, linear and nonlinear systems, function approximation, numerical integration, the EM algorithm, simulation, and document preparation. Fall.
740 STATISTICAL METHODS FOR GENETIC ASSOCIATION STUDIES. Prerequisite, permission of the instructor. This course provides a survey of the statistical methods that have been recently developed for the analysis of genetic association studies, including genomewide association studies and next-generation sequencing studies. Spring.
752 DESIGN AND ANALYSIS OF CLINICAL TRIALS (3) Prerequisites, BIOS 660, and 661 or permission of the instructor. Description: This course will introduce the methods used in clinical trials. Topics include dose-finding trials, allocation to treatments in randomized trials, sample size calculation, interim monitoring, and non-inferiority trials. Fall.
756 INTRODUCTION TO NONPARAMETRIC STATISTICS (STAT 171) (3). Prerequisite, BIOS 661 or equivalent. Theory and application of nonparametric methods for various problems in statistical analysis. Includes procedures based on randomization, ranks, and U-statistics. A knowledge of elementary computer programming is assumed. Spring.
758 ADVANCED STATISTICAL METHODS IN BIOMETRIC AND PUBLIC HEALTH (4) Prerequisites, BIOS 660 and 661 or equivalents. Description: A non-measure theoretic introduction to probability theory, random elements, statistics, and stochastic processes. Random walks, Markov chains, Poisson processes and martingales. Exponential family of densities, finite Sample distributions and the need for large sample methods. Basic properties of statistical estimators, Cramer-Rao bound and the Rao-Blackwell theorem. Stochastic convergence and central limit theorems. Slutsky's theorem, transformation of variables and statistics, and variance stabilization. Neyman-Pearson fundamental lemma and finite sample hypothesis testing. Introduction to large sample inference methods. Likelihood ratio, Rao's score, and Wald tests. Statistical inference for categorical data and regression models. Resampling plans. Elements of Bayes methods. Inference in bioassay, dosimetry and environmental studies. Fall.
759 APPLIED TIME SERIES ANALYSIS (3). Prerequisites, BIOS 661 and 663 or equivalents, and permission of the instructor. Topics include correlograms, periodograms, fast Fourier transforms, power spectra, cross-spectra, coherences, ARMA and transfer-function models, spectral-domain regression. Real and simulated data sets are discussed and analyzed using popular computer software packages.
760 ADVANCED PROBABILITY AND STATISTICAL INFERENCE I (4). Prerequisite, BIOS 661 or permission of the instructor. Measure space, sigma-field, Lebesgue measure, measureable functions, integration, Fubini-Tonelli theorem, Radon-Nikodym theorem, probability measure, conditional probability, independence, distribution functions, characteristic functions, exponential families, convergence almost surely, convergence in probability, convergence in distribution, Borel-Cantelli leema, strong law of large numbers, central limit theorem, the Cramer-Wold device, delta method, U-statistics, martingale central limit theorem. Least squares estimation, uniformly minimal variance and unbiased estimation, estimating functions, maximum likelihood estimation, Cramer-Rao lower bound, information bounds, LeCam's lemmas, consistency, asymptotic efficiency, expectation-maximization algorithm, nonparametric maximum likelihood estimation. Fall.
761 ADVANCED PROBABILITY AND STATISTICAL INFERENCE II (4). Prerequisite, BIOS 760 or permission of the instructor. Description: Elementary decision theory, utility, admissibility, minimax rules, loss functions, Bayesian decision theory, likelihood ratio, Wald, and score tests, Neyman-Pearson tests, UMP and unbiased tests, rank tests, contiguity theory, confidence sets, parametric and nonparametric bootstrap methods, jackknife and cross-validation, asymptotic properties of resampling methods. Elements of Stochastic processes, including Poisson process, renewal theory, discrete-time Markov chains, continuous-time Markov chains, Martingales, and Brownian motion. Spring.
762 THEORY OF LINEAR MODELS (4). Prerequisites, BIOS 661 and 663, MATH 547, MATH 416 or 577. Theory and methods for continuous responses. Topics include matrix theory, the multivariate normal distribution, multivariate quadratic forms, estimability, reparameterization, linear restrictions and splines, estimation theory, weighted least squares, multivariate tests of linear hypotheses, multiple comparisons, confidence regions, prediction intervals, statistical power, mixed models, transformations and diagnostics, growth curve models, dose-response models, missing data. Fall.
763 GENERALIZED LINEAR MODEL THEORY AND APPLICATIONS (4). Prerequisite, permission of instructor if non-Bios major. Introduction to the theory and applications of generalized linear models, quasi-likelihoods, and generalized estimating equations. Topics include logistic regression, over-dispersion, Poisson regression, log-linear models, conditional likelihoods, multivariate regression models, generalized mixed models, and regression diagnostics. Spring.
764 ADVANCED SURVEY SAMPLING METHODS (3). Prerequisite, BIOS 664 or equivalent. Continuation of Biostatistics 664 for advanced students: stratification, special designs, multistage sampling, cost studies, nonsampling errors, complex survey designs, employing auxiliary information, and other miscellaneous topics. Fall.
765 MODELS AND METHODOLOGY IN CATEGORICAL DATA (3). Prerequisites, BIOS 661, 663, 665, or equivalents. Theory and application of methods for categorical data including maximum likelihood, estimating equations and chi-square methods for large samples, and exact inference for small samples. Fall
767 LONGITUDINAL DATA ANALYSIS (4). Prerequisite, BIOS 762. Presents modern approaches to the analysis of longitudinal data. Topics include linear mixed effects models, generalized linear models for correlated data (including generalized estimating equations), computational issues and methods for fitting models, and dropout or other missing data. Spring.
771 DEMOGRAPHIC TECHNIQUES II (3). Prerequisites, BIOS 670 and integral calculus. Life table techniques; methods of analysis when data are deficient; population projection methods; interrelations among demographic variables; migration analysis; uses of population models.
772 STATISTICAL ANALYSIS OF MRI IMAGES (3). Prerequisite, BIOS 761, 762 and 763. This course reviews major statistical methods for the analysis of MRI data and its applications in various studies.
773 STATISTICAL ANALYSIS WITH MISSING DATA (3). Prerequisite BIOS 761 and 762. This course will examine fundamental concepts in missing data, including classifications of missing data, missing covariate and/or response data in linear models, generalized linear models, models for longitudinal data, and survival models. Several missing data methodologies will be discussed including maximum likelihood methods, multiple imputation, fully Bayesian methods and weighted estimating equations. Applications in the biomedical sciences will be presented in detail and several cases studies will be examined. Software packages for analyzing missing data include WinBUSG, SAS and R.
777 MATHEMATICAL MODELS IN DEMOGRAPHY (3). Prerequisite, permission of the instructor. A detailed presentation of natality models, including necessary mathematical methods, and applications; deterministic and stochastic models for population growth, migration.
779 BAYESIAN STATISTICS (4). Prerequisite, BIOS 762 or equivalent. Description: This course examines basic aspects of the Bayesian paradigm in the context of observational studies and clinical trials. Topics include Bayes' theorem, the likelihood principle, prior distributions, posterior distributions, and predictive distributions. General topics include Bayesian modeling (including linear, generalized linear, hierarchical, and survival models), informative prior elicitation, model comparisons, Bayesian diagnostic methods, variable subset selection, and model uncertainty. Markov chain Monte Carlo methods for computation are discussed in detail. Fall.
781 STATISTICAL METHODS IN HUMAN GENETICS (GNET 281) (3). Prerequisites, BIOS 661 and 663 or permission of the instructor. An introduction to statistical procedures in human genetics, Hardy-Weinberg equilibrium, linkage analysis (including use of genetic software packages), linkage disequilibrium and allelic association.
783 STATISTICAL METHODS IN QUANTITATIVE GENETICS (3). Prerequisites, BIOS 661 and 663 or permission of the instructor. An introduction to the statistical basis of variation in quantitative traits, with focus on experimental crosses and decomposition of trait variation, linkage map construction, statistical methodologies and computer software for mapping quantitative trait loci. Issues involving whole-genome analysis will be highlighted. Spring
784 INTRODUCTION TO COMPUTATIONAL BIOLOGY (3). Prerequisites, BIOS 661 and 663, or permission of the instructor. Description: molecular biology, the construction of physical and genomic maps, cloning, sequence assembly, sequence analysis, DNA-RNA protein sequence alignment, sequence patterns, hidden Markov models, matching statistics and the Poisson approximation, discovery of functional motifs via likelihood and Monte Carlo Bayesian approaches, modeling secondary structure, computational algorithms, statistical software, applications to cancer.
785 STATISTICAL METHODS FOR DNA MICROARRAY DATA (3). Prerequisites, BIOS 661 and 663, or permission of the instructor. Description: Clustering algorithms, classification techniques, statistical techniques for analyzing multivariate data, analysis of high dimensional data, parametric and semiparametric models for DNA microarray data, measurement error models, Bayesian methods for analyzing microarray data, statistical software for analyzing microarray data, sample size determination in microarray studies, applications to cancer. Fall
791 EMPIRICAL PROCESSES AND SEMIPARAMETRIC INFERENCE (3). Prerequisites: BIOS 761 or consent of instructor. Description: Theory and applications of empirical process methods to semiparametric estimation and inference for statistical models with both finite and infinite dimensional parameters. Topics include the bootstrap, Z-estimators, M-estimators, semiparametric efficiency. credit hours. Spring.
841 PRINCIPLES OF STATISTICAL CONSULTING (3). Prerequisites, BIOS 545 or equivalent and permission of the instructor except for majors in the department. An introduction to the statistical consulting process, emphasizing its nontechnical aspects. Spring.
844 LEADERSHIP IN BIOSTATISTICS (3). Prerequisites, BIOS 841. Using lectures and group exercises, students are taught where and how biostatisticians can offer leadership in both academic and non-academic public health settings. Fall.
|Last updated August 21, 2013|