Biostatistics

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Course Descriptions

 

04214 Design and Analysis of Clinical Trials. 3 credits.
Prerequisites: Statistical Models and Methods I or Concurrent Registration
The clinical trial protocol, sources of bias in clinical trials, blinding, randomization, sample size calculation; phase I, phase II, phase III and hybrid trials; interim analysis, stochastic curtailment, Bayes designs, and administrative issues in study design.

04220 Research Seminar. 1 credit.
Prerequisites: Enrollment in Division of Biostatistics Graduate Program
Students present plans for and analysis of research projects and research data. Projects and examples from classical and current literature are discussed by students and faculty.

04221 Theory of Statistical Consulting. 2 credits.
Prerequisites: Statistical Models and Methods I or Concurrent Registration
Theory of consulting, statistical techniques most often used in consulting, practical experience in the real consulting setting and writing statistical reports.

04222 Statistical Consulting. 3 credits.
Prerequisite: Statistical Models & Methods II
This course is designed for students to gain experience in statistical consulting by working with the biostatistics faculty members on various consulting projects.

04224 Biostatistical Computing. 3 credits.
Prerequisites: Statistical Models and Methods I or Concurrent Registration
This course will cover the details of manipulating and transforming data required for statistical analysis, such as reshaping the data from a per case to a per event within a case and vice-versa. It will also cover the techniques necessary to write functions and macros in both SAS and S-Plus for developing new/modified data analysis methods. Students are expected to be facile in the use of computers before they take this course.

04231 Statistical Models and Methods I. 3 credits.
Prerequisite: Three semesters of calculus and one semester of linear algebra
Models and analyses for count data and contingency tables, basic nonparametric methods including sign, rank-sum and signed-ranks tests, simple linear regression model and inference, checking model assumptions, correlation analysis, one-way and two-way analysis of variance. Emphasis is on models, their application to data and interpretation.

04232 Statistical Models and Methods II. 3 credits.
Prerequisite: Statistical Models and Methods I
Factorial, nested, split-plot and repeated-measures designs, multiple regression and variable selection, multiple comparisons, logistic regression, discriminate analysis, principal components and factor analysis, rates and proportions, introduction to survival analysis.

04233 Statistical Models and Methods III. 3 credits.
Prerequisite: Statistical Models and Methods II
Model diagnostics in regression analysis, influence and leverage, outliers, collinearity, remedies including transformations and ridge regression; Models for discrete data, two-way and multi-way tables, loglinear models, analysis of loglinear models, Mantel-Haenszel test, models for ordinal variables, multinomial response and matched pairs, analysis of repeated response data.

04261 * Statistical Inference I. 3 credits. (UWM Registration: MTHSTAT 761)
Prerequisite: Advanced Calculus
Fundamentals of probability, independence, distribution and density functions, random variables, moments and moment generating functions, discrete and continuous distributions, exponential families, location and scale families, marginal and conditional distributions, transformation and change of variables, multivariate distributions, random samples, convergence concepts, sampling from normal distributions, order statistics.

04262 * Statistical Inference II. 3 credits. (UWM Registration: MTHSTAT 762)
Prerequisite: Statistical Inference I
Point estimation, interval estimation, hypothesis testing, minimal sufficiency and completeness, ancillary statistics, likelihood and invariance principle, asymptotic properties of estimators and likelihood ratio tests, LMP tests, union-intersection tests, pivotal quantities, coverage probability, large sample estimation and testing.

04264 * Time Series Analysis. 3 credits. (UWM Registration: MTHSTAT 546A)
Prerequisites: Statistical Models and Methods II, Statistical Inference II
An introduction to univariate and bivariate time series with emphasis on stationary ARIMA processes, Box-Jenkins model building and forecasting, spectral representation of stationary time series, model testing and diagnostic evaluation, piecewise non-linear models, and bivariate ARMA processes.

04275 Applied Survival Analysis. 3 credits.
Prerequisites: Statistical Models and Methods I
Basic parameters in survival studies; Censoring and truncation, Competing risks; Univariate estimation including the Kaplan-Meier and Nelson-Aalen estimator; tests comparing two or more populations, the log rank test; Semi-parametric regression, the Cox model; Aalen’s additive hazards regression model; regression diagnostics.

04280 * Applied Probability. 3 credits. (UWM Registration: MTHSTAT 571)
Prerequisite: Statistical Inference I
Markov chains in discrete and continuous time, Poisson processes, random walks, branching processes, birth and death processes, queuing systems, applications to survival and other biomedical models.

04285 Introduction to Bayesian Analysis. 3 credits.
Prerequisites: Statistical Models and Methods I
This course introduces basic concepts and computational tools for Bayesian statistical methods. Topics covered include one and two sample inference, regression models and comparison of several populations with normal, dichotomous and count data.

04295 Readings and Research. 1-9 credits.
Prerequisite: Faculty permission
Readings in recent literature and supervised research project.

04313 Advanced Statistical Computing. 3 credits.
Prerequisites: Statistical Models and Methods II, Statistical Inference II, Biostatical Computing
Numerical algorithms useful in biostatistics including likelihood maximization, numerical integration using quadrature and Monte Carlo methods, interpolation using splines, random variate generation methods, data augmentation algorithms, Markov chain Monte Carlo and the Metropolis-Hastings algorithm.

04363: Advanced Statistics I. 3 credits.
Prerequisite: Statistical Inference II
Exponential family of distributions; Likelihood, score, information, MLE; Asymptotics related to likelihood, Wald, Score, and Likelihood Ratio statistics, delta method; Types of likelihoods, e.g. marginal, conditional, and profile likelihood; Generalized Estimating Equations and quasi-likelihood; Multiple Comparisons

04365 Linear Models I. 3 credits.
Prerequisites: Statistical Models and Methods II, Statistical Inference II
Review of matrix algebra and vector spaces; multivariate normal distribution and quadratic forms, least squares estimation, testing nested models, weighted least squares, one-way ANOVA, testing contrasts, multiple comparison, partial and multiple correlation coefficients, polynomial regression, lack-of-fit test. Analysis of experimental design models including randomized complete block, Latin square and factorial designs, analysis of covariance, random effects and estimation of variance components, mixed models, models for spatial data, kriging, maximum likelihood theory for loglinear models, likelihood, prior, and posterior distributions for linear models, hierarchical models and introduction to their Bayesian analysis.

04384 Statistical Genetics. 3 credits.
Prerequisites: Linear Models I, Statistical Inference II
Fundamental elements of mathematical and population genetics, and statistical theory of the methods of human genetic analysis. Topics include Hardy-Weinberg equilibrium, inbreeding, selection, mutation, models for polygenic and multifactorial inheritance, variance components estimation for the genetic analysis of familial aggregation, linkage and segregation analysis, and ascertainment problems.

04385 Advanced Bayesian Analysis. 3 credits.
Prerequisite: Introduction to Bayesian Analysis, Advanced Statistics I, Statistical Models and Methods III
A combination of Bayesian principles and advanced methods; conjugate, conditional conjugate and non-informative priors in the exponential family; Markov chain Monte Carlo methods for generalized linear mixed models, hierarchical models; restricted parameter spaces and censored data; longitudinal and spatio-temporal models; nonlinear models, population pharmacokinetics; examples of Bayesian analysis of complex biomedical models.

04386 Theory of Survival Analysis. 3 credits.
Prerequisite: Statistical Inference II
Analysis of survival data using counting process techniques. Topics include the mathematical theory of counting process, censoring and truncation, estimation of the survival and cumulative hazard functions, extensions of k-sample nonparametric tests to censored and truncated data, proportional hazards and additive hazards regression models.

04391 Special Topics in Biostatistics. 1-3 credits.
This course is designed to cover special topics in Biostatistics that are not covered in regular courses. The topics will depend on the research interests of the instructor and the students.

04399: Doctoral Dissertation. 1-9 credits.
Prerequisite: Faculty permission
Dissertation research and publication of research as necessary for completion of the doctoral dissertation.

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Page Updated 12/04/2013