MICHAEL R. KOSOROK, Chair
Lawrence L. Kupper, Associate Chair
Jianwen Cai (93) Survival Analysis and Regression Models, Clinical Trials, Analysis of Correlated Responses
Joseph G. Ibrahim (11) Bayesian Inference, Missing Data Problems, Bayesian Survival Analysis, Generalized Linear Models, Genomics
William D. Kalsbeek (55) Sample Design, Survey Analysis, Nonsampling Errors
Alan F. Karr, Inference for Stochastic Processes, Image Analysis (Joint Appointment with Statistics)
Gary G. Koch (14) Categorical Data Analysis, Nonparametric Methods
Lawrence L. Kupper (19) Regression Analysis, Statistical Applications in Epidemiology and Environmental Health
Danyu Lin (89) Survival Analysis, Semiparametric Statistical Methods, Clinical Trials
Keith E. Muller (76) Linear and Nonlinear Repeated Measures Models, Study Design
Pranab K. Sen (10) Statistical Inference, Clinical Trials, Multivariate Analysis (Joint Appointment with Statistics)
Chirayath M. Suchindran (29) Statistical Demography
Michael J. Symons (17) Consulting, Bayesian Applications, Statistical Education
Kinh N. Truong (90) Time Series Analysis, Nonparametric Regression, Bootstrap Methods, Hazard Regression, Splines
Lloyd J. Edwards (95) Longitudinal Data Analysis, Measurement Error Models, Clinical Trials
Amy H. Herring (87) Survival Analysis, Missing Data Methods, Environmental Statistics
Anastasia Ivanova (83) Clinical Trials Design, Sequential Design of Binary Response Experiments, Statistical Methodology in Biostatistics
Bahjat Qaqish (94) Generalized Linear Models, Survival Analysis, Statistical Computing
Craig D. Turnbull (26) Public Health Statistics, Research on Perinatal Outcomes and Behavioral Sciences
Fred A. Wright (7) Statistical Genetics
Haibo Zhou (40) Missing/Auxiliary Data, Survival Analysis, Human Fertility
Mayetri Gupta (39) Statistical Methods for Computational Biology, Stochastic Computation and Monte Carlo Methods, Bayesian Inference and Model Selection
Ethan Lange, Genetics
Donglin Zeng (5) High Dimensional Data, Survival Analysis
Fei Zou (4) Statistical Genetics
Mark A. Weaver (46) Clinical Trials
Richard E. Bilsborrow (30) Economic Demography, Demography, Economic Development, Environment
Shrikant I. Bangdiwala (80) Nonparametric Methods, Clinical Trials Methodology, International Health, Injury Prevention
Lloyd E. Chambless (82) Epidemiological Applications, Analysis of Survey Data, Measurement Error
Robert M. Hamer (28) Linear Models, Mixed Models, Clinical Trials
Lisa LaVange (45) Clinical Trials
James D. Hosking (79) Data Management, Multivariate Techniques, Clinical Trials
Paul W. Stewart (84) Linear Models, Distribution Theory, Statistical Inference, Longitudinal Data
John S. Preisser Jr. (89) Categorical Data, Longitudinal Data Analysis
David J. Couper (77) Epidemiological Methods, Longitudinal Data, Data Quality
Diane Catellier (78) Linear Models, Missing Data, Clinical Trials
Petra Buzkova (50) Longitudinal Data, Semiparametric Modeling, Marginal Regression, Biased Sampling, Survival Data
Michael Hudgens (42) Nonparametric Estimation, Group
Testing, Causal Inference, Infectious Diseases
Todd A. Schwartz (13) Categorical Data, Clinical Trials
Charity Moore, Complex Survey Sampling, Models with Incomplete Covariate Data
Jane Monaco (43) Survival Analysis, Correlated Failure Time Data
Katherine J. Roggenkamp (3) Statistical Computing
John P. Creason, Statistical Applications in Environmental Health, Dose-Response Methodology
Margaret R. Burchinal, Longitudinal Data Analysis, Mixed Models, Child Development Research
Joseph K. Haseman, Statistical Methods in Environmental Health, Toxicology, and Cancer
Daniel G. Horvitz, Sample Survey Design, Nonsampling Errors in Surveys
Norman L. Kaplan, Stochastic Processes, Statistical Genetics
Herman E. Mitchell, Clinical Trials, Health Care Research, Clinical Epidemiology
Christopher J. Portier, Design and Analysis of Environmental Health Research Studies
Ibrahim A. Salama (38) Nonparametric Statistics, Order Statistics, Ergodic Theory
Babubhai V. Shah (49) Survey Data Analysis Software, Multivariate Data Analysis, and Quality Assurance
Clarice R. Weinberg, Statistical Methods in Epidemiology and Environmental Health, Reproductive Epidemiology
J. Michael Bowling, Survey Methodology, Evaluation, Injury Prevention
Kerrie E. Boyle, Demographic Models, Survey Statistics
David B. Dunson, Bayesian Methods, Latent Variables, Nonparametric Processes, Model Uncertainty, Correlated and Multivariate Data, Reproductive Epidemiology, Bioinformatics
Katherine L. Monti, Clinical Trials, Mixed Models
Timothy M. Morgan, Clinical Trials, Survival Analysis, Cancer Statistical Methods
R. Woodrow Setzer, Environmental Statistics, Risk Assessment, Toxicology
Steven M. Snapinn, Statistics in the Pharmaceutical Industry
Maura E. Stokes, Categorical Data Analysis
Donald C. Trost, Statistics in the Pharmaceutical Industry, Statistical Genomics, Multivariate Analysis
Sonia M. Davis, Bioequivalence, Statistics in the Pharmaceutical Industry
Hrishikesh Chakraborty, HIV/AIDS
Christopher S. Coffey, Adaptive Designs, Internal Pilots
Ralph B. D’Agostino, Measurement Error, Clinical Trials, Missing Data, Statistical Genetics
Ralph A. DeMasi, Statistical Methodology
Hongbin Gu
William D. Irish
Kerry B. Hafner, Statistics in the Pharmaceutical Industry, Design and Analyses of Crossover Trials, Repeated Measures Designs
Robert H. Lyles, Environmental Statistics, Measurement Error Models, Statistical Methods in Epidemiology
Henry S. Lynn, Statistical Methods in Clinical Epidemiology, Clinical Trials
Sandra S. Stinnett, Statistical Consulting and Education, Epidemiologic Methods
Douglas J. Taylor (12) Child Development, Environmental Health Statistics, Sexually Transmitted Diseases, Repeated Measures Analysis
Russell D. Wolfinger, Statistical Computation
Dennis D. Wallace
James R. Abernathy
Regina C. Elandt-Johnson
James E. Grizzle
Ronald W. Helms
Barry H. Margolin
Dana E. Quade
Richard H. Shachtman
H. Bradley Wells
511 [111] INTRODUCTION TO STATISTICAL COMPUTING AND DATA MANAGEMENT (3). Prerequisite, previous or concurrent course in applied statistics or permission of the instructor. Introduction to use of computers to process and analyze data, components of digital computers, characteristics of magnetic storage devices, use of JCL and utility programs, concepts and techniques of research data management, use of statistical program packages and interpretation. Fall.
540 [140] 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 [141] QUANTITATIVE METHODS FOR HEALTH CARE PROFESSIONALS I (4). Prerequisite, permission of the 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.
542 [142] QUANTITATIVE METHODS FOR HEALTH CARE PROFESSIONALS II (4). Prerequisites, BIOS 541 and permission of the instructor. Continuation of BIOS 541; main emphasis is on logistic regression; other topics include exploratory data analysis and survival analysis. Spring.
545 [145] 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 the School of Public Health. Continuation of Biostatistics 600; the analysis of experimental and observational data, including multiple regression, and analysis of variance and covariance. Fall and spring.
550 [150] BASIC ELEMENTS OF PROBABILITY AND STATISTICAL INFERENCE I (GNET 636) (3). 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.
551 [151] BASIC ELEMENTS OF PROBABILITY AND STATISTICAL INFERENCE II (3). Prerequisites, BIOS 550 or equivalent, a basic familiarity with a statistical software package (preferably SAS) that has the capacity to do multiple linear regression analysis, or permission of the instructor. The theory and application of multiple linear regression and related analysis of variance (ANOVA) methods. The theory and application of maximum likelihood-based modeling methods, including logistic regression and Poisson regression. Spring.
600 [110] 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.
660 [160] 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 [161] 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 [162] 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 [163] 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 [164] SAMPLE SURVEY METHODOLOGY (STAT 358) (3). 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 [165] ANALYSIS OF CATEGORICAL DATA (3). Prerequisites, BIOS 545, 550, and 662, or permission of the instructor. 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 [166] 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. On demand.
667 [167] APPLIED STOCHASTIC PROCESSES (3). Prerequisite, BIOS 661 or equivalent. Markov chains, Poisson processes and extensions, epidemic models, branching processes and other stochastic models of empirical processes. Disease, population, and other biostatistical applications. Spring.
668 [168] DESIGN OF PUBLIC HEALTH STUDIES (3). Prerequisites, BIOS 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 [170] 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 [180] 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 [191] FIELD OBSERVATIONS IN BIOSTATISTICS (1). Field visits to, and evaluation of, major nonacademic biostatistical programs in the Research Triangle area. (Field fee: $25.) Fall.
735 [235] 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. Spring.
740 [240] SPECIALIZED METHODS IN HEALTH STATISTICS (1 or more). Prerequisite, permission of the instructor. Statistical theory applied to special problem areas of timely importance in the life sciences and public health. Lectures, seminars, and/or laboratory work, according to the nature of the special area under study. Fall, spring, and summer.
750 [250] ADVANCED TECHNIQUES IN BIOMETRY (1-4). Prerequisites, BIOS 661, 663 or equivalents, permission of the instructor. Up to three or four separate one-semester-hour modules presenting advanced techniques in biometry (topics covered usually vary at each offering). A knowledge of elementary computer programming is assumed. Fall, spring, and summer.
756 [256] INTRODUCTION TO NONPARAMETRIC STATISTICS (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.
758 [258] ADVANCED STATISTICAL METHODS IN BIOMETRIC AND PUBLIC HEALTH (4) Prerequisites, BIOS 660 and 661 or equivalents. 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 [259] 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. Spring.
760 [260] ADVANCED PROBABILITY AND STATISTICAL INFERENCE I (3). Prerequisite, BIOS 661 or permission of the instructor. Measure space, sigma-field, Lebesgue measure, measurable 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 [261] ADVANCED PROBABILITY AND STATISTICAL INFERENCE II (3). Prerequisite, BIOS 760 or permission of the instructor. 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 [262] ADVANCED LINEAR MODELS I (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 [263] GENERALIZED LINEAR MODEL THEORY AND APPLICATIONS (4). Prerequisite, permission of the instructor if non-Biostatistics 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 [264] ADVANCED SURVEY SAMPLING METHODS (3). Prerequisite, BIOS 664 or equivalent. Continuation of BIOS 664 for advanced students: stratification, special designs, multistage sampling, cost studies, nonsampling errors, complex survey designs, employing auxiliary information, and other miscellaneous topics. On demand.
765 [265] LINEAR MODELS IN CATEGORICAL DATA ANALYSIS (3). Prerequisites, BIOS 661, 663, 665, and 666 or equivalents. Theory of statistical methods for analyzing categorical data by means of linear models; multifactor and multiresponse situations; interpretation of interactions. Spring.
767 [267] ADVANCED LINEAR MODELS II (4). Prerequisite, BIOS 762. Theory and methods of linear statistical models for continuous response data, including definitions of parameters, hypotheses, isomorphic models, orthogonal polynomials, incomplete/informatively censored data; general linear univariate, multivariate, and mixed (random effects) models and parameterizations for various classes of designed experiments and longitudinal studies; modeling covariance structures. Spring.
771 [271] 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. Spring.
777 [277] 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. Fall. (2000 and alternate years.)
779 [231] BAYESIAN STATISTICS (3). Prerequisite, BIOS 762 or equivalent. Basic aspects of the Bayesian paradigm including Bayes’ theorem, the likelihood principle, prior distributions, posterior distributions, and predictive distributions. Bayesian analysis of linear models, generalized linear models, random effects models, spatial models, and survival models. Informative prior elicitation, model comparisons, Bayesian diagnostic methods, and variable subset selection. Markov Chain Monte Carlo methods for computations. Bayesian methods for the design and analysis of clinical trials. Fall.
780 [280] THEORY AND METHODS FOR SURVIVAL ANALYSIS (3). Prerequisites, BIOS 760 and 761 or permission of the instructor. Counting process-martingale theory, Kaplan-Meier estimator, weighted log-rank statistics, Cox proportional hazards model, nonproportional hazards models, multivariate failure time data. Spring. Lin.
781 [281] 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. Fall.
783 [283] 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 [284] INTRODUCTION TO COMPUTATIONAL BIOLOGY (3). Prerequisites, BIOS 661 and 663, or permission of the instructor. 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. Spring.
785 [285] STATISTICAL METHODS FOR DNA MICROARRAY DATA (3). Prerequisites, BIOS 661 and 663, or permission of the instructor. 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.
841 [341] PRINCIPLES OF STATISTICAL CONSULTING (1). 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.
842 [342] PRACTICE IN STATISTICAL CONSULTING (1-3). Prerequisites, BIOS 511, 545, 550, 841, or equivalents, and permission of the instructor. Under supervision of a faculty member, the student interacts with research workers in the health sciences, learning to abstract the statistical aspects of substantive problems, to provide appropriate technical assistance, and to communicate statistical results. Fall, spring, and summer.
850 [350] TRAINING IN STATISTICAL TEACHING IN THE HEALTH SCIENCES (2 or more). Prerequisite, a minimum of one year of graduate work in statistics. Principles of statistical pedagogy. Students assist with teaching elementary statistics to students in the health sciences. Students work under the supervision of the faculty, with whom they have regular discussions of methods, content, and evaluation of performance. Fall, spring, and summer.
889 [389] RESEARCH SEMINAR IN BIOSTATISTICS (1-3). Prerequisite, permission of the instructor. Seminar on new research developments in selected biostatistical topics. Fall and spring.
990 [390] RESEARCH IN BIOSTATISTICS (2 or more). Individual arrangements may be made by the advanced student to spend part or all of his or her time in supervised investigation of selected problems in statistics. Fall, spring, and summer.
992 [392] MASTER’S PAPER (3 or more). Fall, spring, and summer.
993 [393] MASTER’S THESIS (3 or more). Fall, spring, and summer.
994 [394] DOCTORAL DISSERTATION (Minimum of 3). Fall, spring, and summer.