# Biostatistics Courses

Three views of courses offered by faculty in Biostatistics (BIOS):

**Bios 600 Information**

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- Sample Bios 600 Syllabus

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**Official course descriptions taken from the UNC Graduate Record are below.**

Additional courses may be added on a semester basis at discretion of the department. See UNC Registrar’s site for courses by semester.

*Some titles link to the syllabus for that course. Please note that some syllabi are for past semesters, so dates will not apply to future semesters. The following courses require special arrangements with instructor for both all semesters offered: BIOS 740, 842,850, 992, 993 and 994.
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## Biostatistics > Show Details

Academic credit for approved internship experience.

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. Monaco

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. Johnson

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. Edwards.

550 BASIC ELEMENTS OF PROBABILITY AND STATISTICAL INFERENCE I (GNET 150) (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. Truong

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. Sen, Herman-Giddens..

Required preparation, knowledge of basic descriptive statistics. Major topics include elementary probability theory, probability distributions, estimation, tests of hypotheses, chi-squared procedures, regression, and correlation.

Required preparation, knowledge of basic descriptive statistics. Major topics include elementary probability theory, probability distributions, estimation, tests of hypotheses, chi-squared procedures, regression, and correlation.

Required preparation, knowledge of basic descriptive statistics. Major topics include elementary probability theory, probability distributions, estimation, tests of hypotheses, chi-squared procedures, regression, and correlation.

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. Not offered 2014-15

660 PROBABILITY AND STATISTICAL INFERENCE I (3). Prerequisite, MATH 233 or equivalent. Probability theory; discrete and continuous random variables; expectation theory; bivariate and multivariate distribution theory; regression and correlation; linear functions of random variables; theory of sampling. Fall. Ivanova

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. F. Lin

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. Hudgens

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. TBA

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. Asafu-Adjei

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. Koch and Schwartz

**667 APPLIED LONGITUDINAL DATA ANALYSIS (3).** Prerequisite: analysis of variance and (multiple) linear regression at the level of Bios 545 and/or Bios 663. Familiarity with matrix algebra is also useful. Univariate and multivariate repeated measures analysis of variance, general linear model for longitudinal data, linear mixed model, generalized linear and population-averaged models for non-normal responses. Estimation and inference, maximum and restricted maximum likelihood, fixed and random effects, balanced and unbalanced data. Fall. Edwards

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. Not offered 2014-15.

669 WORKING WITH DATA IN A PUBLIC HEALTH RESEARCH SETTING (3). Prerequisite, BIOS 511, EPID 700, or permission of the instructor. This course provides a conceptual foundation and practical training to students who will be working with data from clinical trials or other public health research studies. Topics include data issues in study design, collecting high quality data, using SAS and SQL to transform data into structures useful for analysis, producing typical reports, data closure and export, and working with big data. Spring. Roggenkamp

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. Suchindran and Bilsborrow.

672 PROBABILITY AND STATISTICAL INFERENCE I (4). 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. Taylor’s series , Riemann, Stieltjes and Lebesgue integration, complex variables and Laplace transforms.

673 Probability and Statistical Inference II (4). Prerequisite, BIOS 660. Permission of the instructor for students lacking the prerequisite. Distribution of functions of random variables; central limit theorem and other asymptotic theory; estimation theory; hypothesis testing; Neyman-Pearson Theorem, likelihood ratio, score, and Wald tests; noncentral distributions. Advanced problems in statistical inferences, including information inequality, best unbiased estimators, Bayes estimators, asymptotically efficient estimation, nonparametric estimation and tests, simultaneous confidence intervals. 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. Zhou

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, Monaco

700 RESEARCH SKILLS IN BIOSTATISTICS (1). Prerequisites, either completion of BIOS 760 and 761 762, and 767 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. Fall. Howard.

735 STATISTICAL COMPUTING – BASIC PRINCIPLES AND APPLICATIONS (3). Prerequisites, BIOS 660, 661, 662, and 663; one programming class at the undergraduate level or equivalent training. This class teaches important concepts and skills for statistical software development using case studies. Topics include: C++ language basics, searching and sorting (hash functions, maps, linear and binary searches), software design and documentation, software compiling, testing, debugging, distribution, and maintenance, and some specific programming techniques such as recursion, enumeration, dynamic programming, and some machine linear methods such as penalized regression, clustering and classification. Fall, Sun, Chen, Qaqish and Li.

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.

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. Not offered 2015.

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 lemma, 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.Zeng.

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. Zou.

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. Not offered 2014-15.

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, Herring.

780 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. Fall. Lin.

781 STATISTICAL METHODS IN GENETIC MAPPING (4). Prerequisites, BIOS 661 and 663 or permission of the instructors. An introduction to statistical methods commonly used in analyzing animal, plant and human genetic data, with a focus on decomposition of trait variation, linkage analysis, disease mapping and association studies. Specifically, the course covers 1) basic population and quantitative genetic principles, including classical genetics, chromosomal theory of inheritance, and meiotic recombination; 2) QTL mapping methods of complex quantitative traits and linkage methods to detect co-segregation with disease; 3) methods for assessing marker-disease linkage disequilibrium, including case-control approaches, and 4) methods for genome-wide association and stratification control. Fall. Zou.

782 Statistical Methods in Genetic Association Studies (3) Prerequisites, permission of the instructor. 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 molecular and population genetics, candidate-gene and 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, trait-dependent sampling, variable selection, and risk prediction. This course is targeted primarily at the PhD students and will be taught at a rigorous statistical level. The students will learn the theoretical justifications for the methods as well as the skills to apply them to real studies. They will also be exposed to current research topics and open problems. Not offered 2014-15

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. Not offered 2014-15.

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. Not offered 2014-15.

841 PRINCIPLES OF STATISTICAL CONSULTING (3). Instructor consent if not a major in the department. Familiarity with either SAS and/or R will be assumed. Students must have completed all courses required for their current degree program or be currently enrolled in remaining required courses. An introduction to the statistical consulting process, the goal of this course is to develop in each student the skills necessary for being a statistical collaborator/consultant of the highest caliber. Emphasized topics include problem solving, study design, data analysis, ethical conduct, teamwork, career paths, data management, and both written and oral communication with scientists and other potential collaborators. Spring. Bangdiwala and Stewart.

842 PRACTICE IN STATISTICAL CONSULTING (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.

843 SEMINAR IN BIOSTATISTICS (1). Fall and spring. Staff.

844 LEADERSHIP IN BIOSTATISTICS (3). Prerequisites, BIOS 841. Using lectures, guest speakers and group exercises, students are taught fundamentals of leadership, plus where and how biostatisticians can offer leadership in both academic and non-academic public health settings. Topics include leadership styles, 1-on-1 communication, strategic planning, motivation, team management, presentation skills, financial leadership, negotiation, decision making, work-life balance, and more. Guest speakers are biostatisticians in prominent leadership roles in industry, government, academia, and service. Fall. Davis.

850 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.

990 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 MASTER’S PAPER (3 or more). Fall, spring, and summer

994 DOCTORAL DISSERTATION (Minimum of 3). Fall, spring, and summer.