Curriculum
Courses
Faculty

500 Level

PBHL-P 510 Introduction to Public Health (3 cr.) Students will learn the basic foundations and disciplines of public health. Explore the public health impact where populations live, work and play will be covered. Students will develop tools to examine issues and create solutions through a public health lens.

PBHL-P 517 Fundamentals of Epidemiology (3 cr.) This course will introduce students to basic epidemiologic concepts including determinants of health and patterns of disease in populations, population health descriptive techniques, use of health indicators and secondary data sources. Students will gain an understanding of the role of Epidemiology in developing prevention strategies and policy. Among the topics to be covered are measures of mortality and morbidity, design and analysis of observational studies, community health assessment and program evaluation.

PBHL-P 551: Biostatistics for Public Health (3 cr.) This course introduces the basic principles and methods of data analysis in public health biostatistics. Emphasis is placed on public health examples as they relate to concepts such as sampling, study design, descriptive statistics, probability statistical distributions, estimation, hypothesis testing, chi-square tests, analysis of variance, linear regression and correlation.

PBHL-B 552: Fundamentals of Data Management (3 cr.) This course teaches concepts related to research data planning, collection, storage, processing, and dissemination. The curriculum includes theoretical guidelines and practical tools for conducting public health research. Hands-on training with real-world examples and problem-solving exercises in SAS will be used to ensure that students are comfortable with all concepts.

PBHL–B 561: Introduction to Biostatistics I (3 cr.) P: consent of instructor. This course introduces the basic principles and methods of data analysis in public health biostatistics. Emphasis is placed on public health examples as they relate to concepts such as sampling, study design, descriptive statistics, probability, statistical distributions, estimation, hypothesis testing, chi-square tests, t- tests, analysis of variance, linear regression and correlation.  SAS software is required for some of the homework questions.

PBHL–B 562: Biostatistics-Public Health II (3cr.) P: PBHL–B 561 or equivalent. This course introduces the advanced principles and methods of data analysis in public health biostatistics.  Emphasis is placed on public health examples as they relate to concepts such as:  Multiple regression, analysis of variance and covariance, logistic regression, nonparametric statistics, survival analysis, epidemiology statistics, and repeated measures analysis.  

PBHL–B 571 Biostatistics Method I-Linear Regression Model (3 cr.) P: PBHL–B 561 or equivalent. It course covers fundamental methods in Experiment Design, ANOVA, Analysis of Covariance, Simple and Multiple Linear Regressions with applications in biomedical study and public health. The focus of this course is to prepare students with solid skill in data analysis and interpretation of analytic results for numerical outcomes.  Extensive use of Statistical software SAS is anticipated.

PBHL–B 572: Biostatistics Method II-Categorical Data Analysis (3 cr.) P: PBHL–B 571 or equivalent. This course covers applied statistical methods for the analysis of categorical data with special emphasis on data collected from epidemiologic studies and general biomedical studies in various designs such as prospective cohort and retrospective case-control designs. The focus of this course is to prepare students with solid skill in data analysis and interpretation of analytic results for binary, multilevel and count data. Extensive use of Statistical software SAS is anticipated.

PBHL–B 573: Biostatistics Method III-Applied Survival Data Analysis 3 cr.) P: PBHL–B 571, 572 or equivalent This course covers basic components in modern survival data analysis with emphasis on its application in biomedical research and public health. It includes the topics of types of censoring and truncation, life tables and survival function estimation, nonparametric log-rank test, parametric accelerated failure time model, semiparametric Cox proportional hazards model and extended Cox regression for time-dependent variables, competing risks and correlated survival data.  The focus of this course is to prepare students with solid skill in data analysis and interpretation of analytic results for time-to-event data. Extensive use of statistical software SAS is anticipated.

PBHL–B 574 Biostatistics Method IV-Applied Longitudinal Data Analysis (3 cr.) P: STAT 51200, 52500 or PBHL–B 571, 572 or permission of instructor. Covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data. Topics: repeated measures ANOVA, random effects and growth curve models, generalized estimating equations (GEE) and generalized linear mixed models (GLMMs). Extensive use of statistical software, e.g. SAS, R.

PBHL–B 581 Biostatistics Computing (3 cr.) P: consent of instructor. The objective of this course is to prepare students with the necessary SAS skills for general data preparation, description, visualization, and some advanced skills. This course may be viewed as computing preparation for Biostatistics methods courses. Data steps and the following procedures will be covered: IMPORT, SORT, PRINT, FORMAT, TABULATE, REPORT, MEANS, UNIVARIATE, FREQ, CORR, SQL, GPLOT, SGPLOT, SGPANEL. SAS macro, ODS and IMLwill also be briefly introduced.

PBHL–B 582 Introduction to Clinical Trials (3 cr.) P: STAT 51200, exposure to survival analysis; or consent of instructor. Prepares biostatisticians for support of clinical trial projects. Topics: fundamental aspects of the appropriate design and conduct of medical experiments involving human subjects including ethics, design, sample size calculation, randomization, monitoring, data collection analysis and reporting of the results.

PBHL–B 583 Applied Multivariate Statistical Methods for Public Health (3 cr.) P: PBHL–B 551, 652 or equivalent.  This is an introductory applied multivariate statistics course designed specifically for graduate students with a PhD major in epidemiology (or advanced masters epidemiology students). The course can also be taken by other non-statistician majors, for example, PhD students in other medical sciences and health care professionals. Students are expected to have taken two previous courses in statistics (introductory and intermediate) covering up through t-test, ANOVA, ANCOVA, linear regression, and logistic regression. The overall objective of this course is to use public health examples while introducing classic multivariate statistical techniques. The course will focus on applications using the SAS software. Very little attention will be given to matrix algebra. Instead, greater importance will be placed on conceptual understanding and interpretations. Basic bivariate statistics, data screening (e.g., missing data, outliers, assumptions, multi-collinearity), and regression will be reviewed. The following classic multivariate techniques will be covered: canonical correlation, MANOVA, MANCOVA, discriminant analysis, principal components analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling (SEM). Two special topics will be introduced but not tested over: (1) mixed linear models for repeated measures analysis and multi-level modeling of clustered data; and (2) analysis of sample survey data, obtained from complex sampling designs, using the SAS SURVEY procedures with sampling weights.

PBHL–B 584 Biostatistical Practicum (1-3 cr.) P: STAT52100; PBHL–B 582, 574; or consent of instructor. Real-world projects in biostatistics involving participation in consulting sessions, directed reading in the literature, research ethics, design of experiments, collection of data and applications of biostatistical methods. Detailed written and oral reports required. May be repeated up to 6 credits.

STAT-I 512 Applied Regression (3 cr.) P: STAT 51100 or equivalent. Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance. Use of existing statistical computing package.

STAT-I 516: Basic Probability Applications (3 cr.) P: MATH-I261. A first course probability, intended to serve as a background for statistics and other applications. Sample space and axioms of probability, discrete and continuous random variables, conditional probability and Bayes’ theorem, joint and conditional probability distributions, expectations, moments and moment generating functions, law of large numbers and central limit theorem.

STAT-I 519 Introduction to Probability (3 cr.) P: MATH26100 or equivalent. Algebra of sets, sample spaces, combinatorial problems, conditional probability, independence, random variables, distribution functions, characteristic functions, special discrete and continuous distributions, distributions of function of random variables, limit theorems.

STAT-I 517: Statistical Inference (3 cr.) A basic course in statistical theory covering standard statistical methods and heir application. Estimation including unbiased, maximum likelihood, and moment estimation; testing hypothesis for standard distributions and contingency tables; confidence intervals and regions; introduction to nonparametric tests and linear regression.

STAT-I 525 Generalized Linear Model (3 cr.) P: STAT52800 or equivalent or consent of instructor. Generalized linear models, likelihood methods for data analysis, diagnostic methods for assessing model assumptions. Methods covered include multiple regression, analysis of variance for completely randomized designs, binary and categorical response models, and hierarchical log-linear models for contingency tables.

STAT-I 528 Mathematical Statistics I (3 cr.) P: STAT51900 or equivalent. Sufficiency and completeness, the exponential family of distributions, theory of point estimation, Cramer-Rao inequality, Rao-Blackwell Theorem with applications, maximum likelihood estimation, asymptotic distributions of MLestimators, hypothesis testing, Neyman-PearsonLemma, UMP tests, generalized likelihood ratio test, asymptotic distribution of the GLR test, sequential probability ratio test.

STAT-I 536 Introduction to Survival Analysis (3 cr.) P: STAT 51700 or equivalent. Deals with the modern statistical methods for analyzing time-to-event data. Background theory is provided, but the emphasis is on the applications and the interpretations of results. Provides coverage of survivorship functions and censoring patterns; parametric models and likelihood methods, special lifetime distributions; nonparametric inference, life-tables, estimation of cumulative hazard functions, the Kaplan-Meier estimator; one and two-sample nonparametric tests for censored data; semiparametric proportional hazards regression (Cox Regression), parameters’ estimation, stratification, model fitting strategies and model interpretations. Heavy use of statistical software such as Splus and SAS.

600 Level

PBHL-B 616 Advanced Statistical Computing (3 cr.) This course will cover selected computational techniques useful in advanced statistical applications and statistical research. Topics to be covered include methods for solving linear equations, numerical optimization, numerical integration, Expectation-Maximization (EM) algorithm, Monte Carlo method, Bayesian methods, bootstrap methods and stochastic search algorithms.

PBHL-B 626 Advanced Likelihood Theory (3 cr.) This course covers theoretical foundation of statistical inference with focus on likelihood theory and its application on biomedical studies. It provides a good preparation for advanced biostatistics courses such as Advanced GLM, Advanced Longitudinal Data Analysis, and Advanced Survival Analysis.

PBHL-B 636 Advanced Survival Analysis (3 cr.) P: STAT 53600, 62800, or PBHL–B 626 or equivalent.  Addresses the counting process approach to the analysis of censored failure time data. Standard statistical methods in survival analysis will be examined, such as the Nelson-Aalen estimator of the cumulative hazard function, the Kaplan-Meier estimator of the survivor function, the weighted log-rank statistics, the Cox proportional hazards regression model, and the accelerated failure time model.

PBHL–B 646 Advanced Generalized Linear Model (3 cr.) P: STAT52500 or equivalent. This course focuses on the key concepts and theoretical underpinnings of generalized linear models (GLM). It describes the basic modeling structure, theoretical properties of parameter estimates, and model fitting approaches in the context of GLM. It also covers some of the more recent extensions of GLM. 

PBHL-B 656 Advanced Longitudinal Data Analysis (3 cr.) P: PBHL–B 574 or equivalent. The theory of classical and modern approaches to the analysis of clustered data, repeated measures, and longitudinal data: random effects and growth curve models, generalized estimating equations, statistical analysis of multivariate categorical outcomes, estimation with missing data. Discussion of computational issues: EM algorithm, quasi-likelihood methods, Bayesian methods for both traditional and new methodologies.

PBHL-B 698 Topics in Biostatistical Methods (1-3 cr.) P: Consent of instructor. Directed study and reports for students who wish to undertake individual reading and study on approved topics.

PBHL-B 800 Research-Ph.D. Thesis (1---15 cr.) P: Must have been admitted to candidacy. See advisor for more information. Research required by the graduate students for the sole purpose of writing a Ph.D. Dissertation.

STAT-I 619 Probability Theory (3 cr.) P: STAT 51900, 52800 or equivalent. Theory Measure theory based course in probability. Topics include Lebesgue measure, measurable functions and integration. Radon-Nikodym Theorem, product measures and Fubini’s Theorem, measures on infinite product spaces, basic concepts of probability theory, conditional probability and expectation, regular conditional probability, strong law of large numbers, martingale theory, martingale convergence theorems, uniform integrability, optional sampling theorems, Kolmogorov’s Three series Theorem, weak convergence of distribution functions, method of characteristic functions, the fundamental weak compactness theorems, convergence to a normal distribution, Lindeberg’s Theorem, infinitely divisible distributions and their subclasses.

STAT-I 628 Advanced Statistical Inference (3 cr.) P: STAT 51900, 52800, C: STAT 61900 or equivalent.. Real analysis for inference, statistics and subfields, conditional expectations and probability distributions, UMP tests with applications to normal distributions and confidence sets, invariance, asymptotic theory of estimation and likelihood based inference, U-statistics, Edgeworth expansions, saddle point method.