Course Descriptions and Availability

Brief course descriptions, including the sessions these courses are typically offered, for Biostatistics courses and selected other courses of special interest to Biostatistics students. For more detailed information about a specific course, contact the course instructor or the Graduate Program Coordinator.

Biostatistics Courses

171:161 Introduction to Biostatistics, 3 s.h.

Introduction to the application of statistical techniques to biological data.  Topics include descriptive statistics, probability, binomial, Poisson, and normal distributions, sampling distributions; tests of significance, confidence intervals, analysis of  frequency data, and simple linear regression.  Prerequisite: college algebra.  Offered fall and spring semesters and summer session.

171:162 Design and Analysis of Biomedical Studies, 3 s.h.

Simple and multiple linear regression and correlation; one- and two-way layout considerations in planning experiments; factorial experiments; multiple comparison techniques; orthogonal contrasts. Prerequisites: 171:161 or equivalent. Same as 22S:140.  Offered spring semesters.

171:164 Research Data Management, 3 s.h.

Overview of problems encountered in gathering and processing data from biomedical investigations; introduction to various data management techniques useful in biomedical studies; introduction to Microsoft Access. Prerequisite: Fortran or C programming capability.  Offered fall semesters of odd years.

171:173 Intermediate Design of Sample Surveys, 3 s.h.

Intermediate course dealing with challenges encountered in designing sample surveys, including construction and number strata, unbiased ratio estimators, multi-staged sampling, variance estimation in complex surveys,  double sampling, sampling frame construction, panel studies, and problems due to non-response. Prerequisites: 171:202, 22S:154 or 22S:194 or equivalent.  Offered fall semesters.

171:174 Introductory Longitudinal Data Analysis, 3 s.h.

Introduction to statistical models and estimation methods that can be used to analyze correlated data, such as when the same subject is measured repeatedly.  Use of statistical software is emphasized.  Prerequisite:  171:161 or equivalent, or consent of instructor.  Pre- or co-requisite:  171:162 or 22S:152, or consent of instructor.

171:178 Biostatistical Computing, 3 s.h.

The course is designed for Biostatistics students, with some C—C++ skills, to build a solid ground work in SAS and R programming with emphasis on data management, Monte Carlo simulations and expectation maximization techniques. Co-requisite: 171:201. Offered fall semesters.

171:201 Biostatistical Methods I, 4 s.h.

Problem-oriented probability distributions, moments, estimation, parametric and nonparametric inference for one-sample and two-sample problems, analysis of frequency data, linear regression, correlation analysis; emphasis on using computers. Prerequisites: two semesters of calculus; linear algebra, consent of instructor.  Offered fall semesters.

171:202 Biostatistical Methods II, 4 s.h.

Continuation of 171:201, which is prerequisite:  linear regression correlation, multiple linear regression, multiple factor experiments, multiple comparisons, orthogonal contrasts, block and split-plot designs, confounding, interactions, analysis of covariance, mixed models.  Prerequisite: 171:201.  Offered spring semesters.

171:203 Biostatistical Methods in Categorical Data, 3 s.h.

This course provides an introduction to methods for applied categorical data analysis including estimation of proportions, rates, and risks; measures of relative risk and odds ratios; stratified analysis; case control studies; and logistic regression.  Prerequisites: 171:201, 171:178. Co-requisite: 171:202, 22S:154 or 22S:194.  Offered spring semesters.

171:230 Statistical Data Mining in Public Health, 3 s.h.

This course introduces a set of supervised statistical methods such as regression, decision tree, neural network, and some unsupervised methods such as association rules, and clustering for the data analysis in health related applications.  Prerequisites: 171:202, 22S:153 or 22S:193 or equivalent.  Offered spring semester of even years.

171:241 Applied Categorical Data Analysis, 3 s.h.

Overview of the methods used to analyze categorical data from health science investigations, including estimation of rates and risks, measures of relative risk, stratified analysis, and logistic regression analysis.  Prerequisites: 171:162, 173:140.  Offered fall semesters.

171:242 Applied Survival and Cohort Data Analysis, 3 s.h.

Nonparametric and semiparametric methods for survival data; methods of comparing directly standardized rates and standardized mortality ratios; Poisson regression for cohort data.  Prerequisites: 171:241.  Offered spring semesters of odd years.

171:243 Cohort Data Analysis, 1 s.h.

Methods of comparing directly standardized rates and standardized mortality ratios; Poisson regression for cohort data.  Prerequisites: 171:241, consent of instructor.  Offered spring semesters of odd years.

171:251 Theory of Biostatistics I, 4 s.h.

Intermediate level study of sufficiency, exponential families, methods of estimation, uniform minimum variance unbiasedness, information, likelihood theory, confidence intervals, Neyman-Pearson lemma, and asymptotic theory and its applications.  Prerequisites: 22S:154 or 22S:194, 171:202, or equivalent.  Offered fall semester of even years.

171:252 Theory of Biostatistics II, 4 s.h.

Nonparametric hypothesis tests, semiparametric estimation, generalized linear models and related topics, EM algorithm, computer-intensive methods; application of theory of 171:251 to classical and new methods in biostatistics.  Prerequisites: 171:251.  Offered spring semester of odd years.

171:261 Survival Data Analysis, 3 s.h.

Types of censoring and truncation; survival function estimation; life tables; parametric inference using exponential, Weibull and accelerated failure time models; nonparametric tests; sample size calculation; Cox regression with stratification and time-dependent covariates; regression diagnostics; competing risks; analysis of correlated survival data.  Prerequisites: 22S:154 or 22S:194, 171:202, or equivalent. Same as 22S:225.  Offered fall semesters.

171:262 Analysis of Categorical Data, 3 s.h.

Models for discrete data, distribution theory, maximum likelihood and weighted least squares estimation for categorical data, tests of fit, model selection.  Prerequisite: 22S:154 or 22S:194, 22S:164 or 171:202, or equivalent. Same as 22S:220.  Offered spring semesters.

171:264 Longitudinal Data Analysis, 3 s.h.

Introduction to statistical methodology for analyzing data from observational and experimental studies in which the response variable from each subject is measured repeatedly.  Use of statistical software packages and specialized programs is emphasized.  Prerequisites: 22S:154 or 22S:194, 171:202, or equivalent.  Offered spring semesters of odd years.

171:266 Statistical Methods in Clinical Trials, 3 s.h.

Surveys statistical methods commonly utilized in clinical trials.  Also provides a methodologic perspective to the design, conduct and analysis of clinical trials.  Phase III randomized controlled clinical trials are emphasized.  Prerequisites: 171:202, 22S:154 or 22S:194, or equivalent.  Offered spring semesters.

171:271 Advanced Survival Analysis, 3 s.h.

Counting process/martingale theory leading to asymptotic results of survival methods; semiparametric regression of the accelerated failure time and additive hazard models; multivariate survival models for clustered, multiple event and recurrent event data; special topics.  Prerequisite:  171:261.

171:280 Preceptorship in Biostatistics, arr.

Individual work experience in using knowledge, skill acquired in classroom; arranged in conjunction with ongoing activities in the department, the College of Public Health or College of Medicine, or off-campus in governmental agency or private industry.  Prerequisite:  Consent of instructor.

171:281 Independent Study in Biostatistics, arr.

In-depth pursuit of an area of special interest in biostatistics requiring substantial creativity and independence.  Prerequisite:  Consent of instructor

171:282 Problems/Special Topics in Biostatistics, arr.

Didactic material in biostatistics that may include tutorial, seminar, faculty-directed independent work (e.g. literature search, project, short research project).  Prerequisite:  Consent of instructor.

171:290 Advanced Biostatistics Seminar, 1, 3 s.h.

Each semester focuses on selected current topics in biostatistics; the seminar is chaired by different faculty members each semester who select the topics and organize core readings; format is a mix of student presentations and open discussion.  Prerequisite: 22S:154 or 22S:194, 171:202, consent of instructor.

171:295 Research in Biostatistics, arr.

For students engaged in research that may lead to a dissertation.  Prerequisite:  consent of instructor.

171:300 Thesis/Dissertation, arr.

Work on Biostatistics Ph.D. dissertation with the dissertation adviser.  Prerequisite:  consent of instructor.

Selected Statistics Courses

Contact the Department of Statistics, 241 Schaeffer Hall, 335-0712, for questions regarding course availability and scheduling.

22S:138 Bayesian Statistics, 3 s.h.

Bayesian statistical analysis, with focus on applications; Bayesian and frequentist methods compared; Bayesian model specification, choice of priors, computational methods; hands-on Bayesian data analysis using appropriate software; interpretation and presentation of analysis results. Prerequisite: 22S:120 or equivalent. Offered fall semesters. Same as 07P:148.

22S:153 Mathematical Statistics I, 3 s.h.

Probability, conditional probability, random variables, distribution and density functions, joint and conditional distributions, various families of discrete and continuous distributions, mgf technique for sums, convergence in distribution, convergence in probability, central limit theorem. Prerequisites: 22S:027 and 22S:028 or equivalents. Offered fall and spring semesters.

22S:154 Mathematical Statistics II, 3 s.h.

Transformations, order statistics, point estimation, sufficient statistics, Rao-Blackwell Theorem, delta method, confidence intervals, likelihood ratio tests, applications. Prerequisite: 22S:153. Offered fall and spring semesters.

22S:156 Applied Time Series Analysis, 3 s.h.

General stationary, nonstationary models, autocovariance autocorrelation functions. Stationary, nonstationary autoregressive integrated moving average models. Identification, estimation, forecasting in linear models. Use of statistical computer packages. Prerequisites: 22S:131, and either 22S:152 or 22S:164. Offered spring semesters.

22S:161 Applied Multivariate Analysis, 3 s.h.

MANOVA, discriminant analysis, factor analysis, principal components, canonical analysis, nonmetric scaling, cluster analysis, categorical data analysis, use of multivariate statistical computer packages. Prerequisites: 22S:152 and 22S:158 or equivalents, and facility with matrix algebra. Same as 07P:245. Offered fall semesters of odd years.

22S:193 Statistical Inference I, 3 s.h.

Review of probability, distribution theory (multiple random variables, moment-generating functions, transformations, conditional distributions), sampling distributions, order statistics, limit theory, principles of data reduction. Offered fall semesters. Prerequisites: 22M:028 and 22S:131, or equivalents.

22S:194 Statistical Inference II, 3 s.h.

Continuation of 22S:193, which is prerequisite, point estimation theory (MLE, Bayes, UMVU), hypothesis testing, interval estimation, decision theory. Offered spring semesters.

22S:195 Probability and Stochastic Processes I, 3 s.h.

Conditional expectations; Markov chains including random walks and gambler’s ruin, classification of states, stationary distributions, and branching processes. Prerequisite: 22S:130 or 22S:120 and consent of instructor. Offered fall semesters.

22S:238 Bayesian Analysis, 3 s.h.

Decision theory, coherence and utility, subjective probability, likelihood principle, conjugate families, structure of Bayesian inference, asymptotic approximations for posterior distributions, sequential experiments, exchangeability, hierarchical models, nonparametric Bayes procedures, empirical Bayes methods, numerical and Markov chain Monte Carlo methods. Offered fall semesters of even years. Prerequisites: 22S:190 and 22S:194.

22S:248 Computer Intensive Statistics, 3 s.h.

Computer arithmetic: random variate generation; numerical optimization; numerical differentiation, integration, and linear algebra; smoothing techniques; bootstrap methods; cross-validation; MCMC; EM and related algorithms; other topics per student/instructor interests. Offered spring semesters of even years. Prerequisites: 22S:164 and proficiency in Fortran or C or C++ or Java.

22S:255 Linear Models, 4 s.h.

Linear spaces and matrix theory, best linear unbiased estimation, multivariate normal distribution and distributions of quadratic forms, full-rank and non-full rank linear models, estimability, interval estimation, hypothesis testing, random and mixed models, applications. Prerequisites: 22S:164, 22S:165, and 22S:194. Offered fall semesters.

22S:256 Multivariate Analysis, 3 s.h.

Multivariate distributions, tests and estimates, multivariate general linear model, MANOVA, discriminant analysis, canonical correlation, factor analysis, principal components. Prerequisite: 22S:255. Offered spring semesters of even years.