Biostatistics Courses
________________________________PH 1610 Introduction to Biostatistics
Credits: 4
Designed for students with little previous coursework in mathematics or statistics. Topics include research ethics, study design, data description, elements of probability, distribution of random variables, applications of the binomial and normal distributions, estimation and confidence intervals, hypothesis testing, contingency tables, regression, and analysis of variance. Additional topics include introduction to statistical computing and data management, distribution free statistical methods, demographic measures, and life tables.
________________________________PH 1620 Introduction to Public Health Research Computing
Credits: 4
This course introduces the use of computers in public health research. Emphasis will be on concepts of research data processing. Topics include microcomputers, operating systems, file management, data entry, and the use of statistical packages for data analysis.
Prerequisites: PH 1610 or consent of instructor.
________________________________PH 1725 Intermediate Biostatistical Methods I
Credits: 4
This course is designed for students whose future work will require extensive data analysis in research problems of public health and the biological sciences. Topics include measurement problems, descriptive statistics, graphics, sampling distributions, hypotheses testing, comparison of samples, non-parametric methods, and applications. Basic design issues are discussed as are ethical considerations in design and analysis. Computer applications are included. Illustrations and applications are selected from research studies.
Prerequisites: A course in calculus or consent of instructor.
This is a designated core course.
PH 1725 must be followed by PH 1726 for the intermediate biostatistics course sequence to be applied to any biostatistics or core course requirement. The completion of PH 1725 by itself does not meet any degree requirement.
________________________________PH 1726 Intermediate Biostatistical Methods II
Credits: 4
This course is a continuation of PH 1725. Topics include single and multiple regression, correlation theory, one and two way classifications for attributes and measurements, analysis of discrete data, and introduction to factorial experiments. Computer applications are included. Illustrations and applications are selected from research studies.
Prerequisites: PH 1725 or consent of instructor.
________________________________PH 1727 Statistical Programming I
Credits: 4
This course will introduce the student to statistical computing. No previous computing experience is necessary. Topics covered will include hardware devices, data storage media, and language types. Data entry, forms design, and data coding will be introduced. Computer-specific job control language will be covered to familiarize the student with operating systems. FORTRAN will be used to demonstrate the concepts of data types, file organization, file structure, record format, sequential programming logic, and mass storage input/output. FORTRAN statement types will be used to demonstrate data-type specification, assignment, input/output, branching, iteration, and subprograms.
Prerequisites: Working knowledge of college algebra or consent of instructor.
________________________________PH 1728 Statistical Programming II
Credits: 4
This course is a continuation of PH 1721, Statistical Programming I. Topics include issues in data collections, processing, analysis, and reporting for various types of studies. Students will be introduced to methods of communicating or interacting with computer software packages, including text query commands, procedure calls, and menu-directed interfaces. A FORTRAN software package will be developed in class to implement simple statistical methods. The techniques used by the package will be compared with techniques used by other statistical software. Typical statistical procedures to be covered include topics such as t-tests, contingency tables and chi-square tests, and multiple regression and contrast tests.
Prerequisites: PH 1727 or consent of instructor.
________________________________PH 1730 Statistical Methods in Epidemiological Research
Credits: 4
This course introduces the statistical methods used in epidemiological investigations. Topics include the identification of sources of bias, incidence and prevalence rates, measures of association in contingency tables, retrospective and prospective study designs, confidence intervals for the odds ratio, combining sets of data using the Mantel-Haenszel Test, techniques for combining evidence from 2x2 contingency tables, matched control studies, standardized rates, life tables, Cox regression, and logistic regression.
Prerequisites: PH 1610 or consent of instructor.
________________________________PH 1745 Sampling Techniques for Health Surveys
Credits: 4
This course introduces the principles and current practices of survey sampling with health-related applications. Topics include basic concepts and practical issues in statistical sampling, design and analysis for common sample designs, including simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling, and analytic issues concerning the use of complex survey data, such as the National Health and Nutrition Examination Survey.
Prerequisites: PH 1726 or consent of instructor.
________________________________PH 1820 Applied Statistical Analysis I
Credits: 4
This course in methods of data analysis is intended for graduate students in biostatistics, and M.S. or Ph.D. students in other disciplines. The course emphasizes the design, implementation, analysis, and reporting of research investigations. Topics include two-sample inference using t-distributions, robustness and resistance, alternatives to the t-test based analyses, comparisons among several samples, linear combinations and multiple comparisons, simple and multiple linear regression methods, regression diagnostics, variable selection, and related methods. The course requires intensive computer analyses of case studies emphasizing graphics and the proper use and interpretation of statistical software packages using Stata as a model statistical software package.
Prerequisites: PH 1726 or consent of instructor.
________________________________PH 1821 Applied Statistical Analysis II
Credits: 4
This course is a continuation of PH 1820. Topics include the analysis of variance for two-way classifications, factorial arrangements and blocking designs, analysis of repeated measures and other multivariate responses, exploratory tools for summarizing multivariate responses, logistic methods for binary response variables and binomial counts, and log-linear regression for Poisson counts, As in PH 1820, emphasis is placed on case studies, graphics, and proper use and interpretation of statistical software packages using Stata as a model statistical software package.
Prerequisites: PH 1820 or consent of instructor.
________________________________PH 1830 Logistic Regression
Credits: 4
This course presents the theory and applications of logistic regression. Topics include the logistic regression model, sampling methods, model building strategies, assessing model fit, conditional logistic regression for matched analyses, polychotomous logistic regression, and Poisson regression.
Prerequisites: PH 1726 or consent of instructor.
________________________________PH 1831 Survival Analysis
Credits: 4
This course presents the theory and applications of survival analysis. Topics include censoring, parametric and nonparametric models, hypothesis testing, proportional hazards model with fixed and time-varying covariates, model building strategies, and assessing model fit.
Prerequisites: PH 1830 or consent of instructor.
________________________________PH 1835 Statistical Methodology in Clinical Trials
Credits: 4
This course covers the use of current statistical methodology in the design, execution, and analysis of clinical trials. Some of the topics include basic study design, randomization, sample size issues, data analysis issues, and interim monitoring. The course is intended primarily for M.S. and Ph.D. biostatistics students and doctoral students minoring in biostatistics.
Prerequisites: PH 1726 and calculus or the consent of instructor.
________________________________PH 1855 Distribution-Free Methods
Credits: 3
This course introduces the theory and applications of distribution-free (non-parametric) statistical methods. Topics include properties of distribution functions, K-S tests, runs tests, rank sum tests, non-parametric analysis of variance, rank correlation, contingency table analysis, and distribution-free confidence intervals.
Prerequisites: PH 1726 or consent of instructor.
________________________________PH 1910 Statistical Theory of Biostatistics I
Credits: 4
Topics include probability theory, distributions of discrete and continuous random variables, mathematical expectation, moments and moment generating functions, distribution of transformed variables, limiting distributions, and estimation. Theoretical results are applied to selected research problems in public health and the biomedical sciences. This course is designed primarily for students specializing in biostatistics.
Prerequisites: Working knowledge of differential and integral calculus.
________________________________PH 1911 Statistical Theory of Biostatistics II
Credits: 4
This course is a continuation of PH 1910. Topics include statistical hypothesis tests, LR tests, Bayes tests, noncentral distribution and power, selected non-parametric tests, sufficiency, completeness, exponential family, and the multivariate normal distribution. Theoretical results are applied to research problems in public health and biomedical sciences.
Prerequisites: PH 1910 or consent of instructor.
________________________________PH 1915 Linear Models I
Credits: 4
This course is an introduction to the fundamentals of linear statistical models for students with preparation in statistical theory and methods. Using matrix algebra, distributions of quadratic forms are presented and used to develop the general linear model for multi-factor data. Topics include estimation and hypothesis testing in the full rank model, estimability and statistical inference in the less than full rank model. Theory and computation are emphasized. This course is intended primarily for students specializing in biostatistics.
Prerequisites: PH 1911 or consent of instructor.
________________________________PH 1916 Linear Models II
Credits: 4
This course introduces a unified theoretical and conceptual framework for many of the most commonly used statistical methods including simple and multiple regression, t-tests and analysis of variance and covariance, logistic regression, log-linear models and several other analytic methods. The topics also include components and inference of a generalized linear model, binomial regression, Poisson regression, methods of handling over dispersion, quasi-likelihood functions and diagnostics for the Generalized Linear Model (GLM). Computational methods will be emphasized. This course is intended for students specializing in biostatistics.
Prerequisites: PH 1911, knowledge of linear models and computational skills.
________________________________PH 1918 Statistical Methods in Correlated Outcome Data
Credits: 4
This course presents extensions of general and generalized linear models to correlated outcome data. Such models arise from hierarchical designs, such as longitudinal studies or sample surveys. Major topics include: mixed linear models for continuous, binomial, and count data; maximum likelihood estimation; generalized estimating equations; REML, EM algorithm; current general and specialized software applicable to these methods; and readings from current statistical literature. This course is intended for students with a background in linear models.
Prerequisites: PH 1916 or consent of instructor.
________________________________PH 1920 Categorical Data Analysis
Credits: 4
This course covers approaches of maximum likelihood, weighted least squares and generalized estimating equations applied to the analysis of contingency tables and other categorical outcomes. It emphasizes the formulation of hypotheses and hypothesis testing through generalized linear models. Special topics include the analysis of matched case-control studies, repeated measurements, and clustered categorical data. Computer programs from SAS are used in the analysis of the data.
Prerequisites: PH 1911 or consent of instructor.
________________________________PH 1950 Stochastic Processes in Biostatistics I
Credits: 4
This course covers the application of stochastic processes to problems in the biological and health sciences. Topics include discrete-time Markov chains, discrete-time branching processes, random walks, estimation of parameters in discrete-time Markov chains with complete or partially observed data, test of the Markov property and test of stationarity, time-reversible Markov chains, basic theory of Markov chains, Monte Carlo methods and its applications, and Poisson processes. Recent developments in related areas and their applications will be explored. Basic statistical theory, especially the estimation methods and EM algorithm, will be reviewed.
Prerequisites: PH 1911 and a thorough knowledge of calculus.
________________________________PH 1951 Stochastic Processes in Biostatistics II
Credits: 4
This course is a continuation of PH 1950. Differential equations and partial differential equations will be briefly reviewed. The main course contents cover several models of continuous-time Markov processes that include the Poisson process, the Yule process, the birth-and-death process, the epidemic process, the queueing process, the illness-death process, and other stochastic models in public health. Statistical inference for some of these models will also be explored. The appropriate data using these models will be analyzed. Applications of counting processes and the concept of Martingale theory to other statistical methods including survival analysis will be introduced. Brownian motion will be briefly discussed.
Prerequisites: PH 1950 or consent of instructor.
________________________________PH 1960 Time Series Analysis
Credits: 4
The uses, descriptions, and analyses of time series models are covered. Methods are developed for fitting models to time series data, and using the fitted models for forecasting future values of the series, as well as for adjusting concomitant variables to control future values of the series. The course also covers spectral and cross spectral methods for analyzing time series data, and sampling distributions of model parameters and of future forecasts. Univariate models are generalized to the case where more than one observation is taken at each time period.
Prerequisites: A course in theoretical statistics or consent of instructor.
________________________________PH 1970 Multivariate Analysis I
Credits: 4
This course is an introduction to the theory and applications of multivariate analysis emphasizing geometric development and interpretation. Topics include perpendicular projections, generalized matrix inverses, the spectral theorem, multivariate densities, moments and characteristic functions, principal components, and the multinormal distribution with associated derived distributions.
Prerequisites: PH 1910 and PH 1911 or equivalent courses in mathematical statistics.
________________________________PH 1971 Multivariate Analysis II
Credits: 4
This course is a continuation of PH 1970. Topics include the Wishart distribution, Jacobians and content, and hypotheses tests on mean vectors and dispersion matrices. Additional topics include the multivariate general linear model, principal components, factor analysis, clustering techniques, discrimination and classification, asymptotic estimation and distribution theory. Applications are selected from public health and the biomedical sciences.
Prerequisites: PH 1970 or consent of instructor.
________________________________PH 1998 Special Topics in Biostatistics
Credits: 1-4
Selected topics provide intensive coverage of biometric theory and applications. Topics vary from semester to semester. Previous topics have included: Advanced Statistical Theory, Bioassay, Current Topics Seminar, Demographic Analysis for Small Areas, Demography and Public Health, Design of Experiments, Introduction to SAS Data Management, Introduction to Spatial Statistics, Operations Research: A Decision Making Process, Sequential Analysis, Statistical Consulting, Statistical Methods for Handling Missing Data, Theoretical Concepts in Statistics with Applications to Public Health.
________________________________PH 1999 Individual Study in Biostatistics
Credits: 1-9
A plan of study is determined for each participating student and supervised by a member of the Biostatistics faculty. In general, courses of individual study are not recommended unless a student has completed the appropriate introductory courses in biostatistics or presents evidence of experience in the field of biostatistics. May be repeated for credit.
________________________________PH 9997 Practicum
Credits: 1-9
A practicum is determined by the student and advisor, and supervised by a member of the Division faculty.
________________________________PH 9998 Culminating Experience/Thesis Research
Credits: 1-9
Thesis research is determined by the student with approval of the student's advisory committee. May be repeated for credit.
________________________________PH 9999 Dissertation Research
Credits: 1-9
Dissertation research is determined by the student with approval of the student's advisory committee. May be repeated for credit.