Biostatistics

Course Descriptions

BIOST 214: Design and Analysis of Clinical Trials

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods I or Concurrent Registration
• Description: 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.

BIOST 220: Research Seminar

• 1 credit, MCW
• Prerequisites: Enrollment in Division of Biostatistics Graduate Program
• Description: 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.

BIOST 221: Theory of Statistical Consulting

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

BIOST 222: Statistical Consulting

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

BIOST 224: Biostatistical Computing

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods I or Concurrent Registration
• Description: 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.

BIOST 231: Statistical Models and Methods I

• 3 credits, MCW
• Prerequisite: Three semesters of calculus and one semester of linear algebra
• Description: 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.

BIOSTAT 232: Statistical Models and Methods II

• 3 credits, MCW
• Prerequisite: Statistical Models and Methods I
• Description: 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.

BIOST 233: Statistical Models and Methods III

• 3 credits, MCW
• Prerequisite: Statistical Models and Methods II
• Description: 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.

BIOST 261: Statistical Inference I

• 3 credits, UWM
• Prerequisite: Advanced Calculus
• Description: 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.

BIOST 262: Statistical Inference II

• 3 credits
• Offered during spring semester at UWM
• Prerequisite: Statistical Inference I
• Description: 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.

BIOST 264: Time Series Analysis

• 3 credits, UWM
• Prerequisites: Statistical Models and Methods II, Statistical Inference II
• Description: 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.

BIOST 275: Applied Survival Analysis

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods I
• Description: 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.

BIOST 280: Applied Probability

• 3 credits, UWM
• Prerequisite: Statistical Inference I
• Description: 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.

BIOST 285: Introduction to Bayesian Analysis

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods I
• Description: 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.

BIOST 295: Readings and Research

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

BIOST 313: Advanced Statistical Computing

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods II, Statistical Inference II, Biostatical Computing
• Description: 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.

BIOST 363: Advanced Statistics I

• 3 credits, MCW
• Prerequisite: Statistical Inference II
• Description: 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

BIOST 365: Linear Models I

• 3 credits, MCW
• Prerequisites: Statistical Models and Methods II, Statistical Inference II
• Description: 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.

BIOST 384: Statistical Genetics

• 3 credits, MCW
• Prerequisites: Linear Models I, Statistical Inference II
• Description: 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.

BIOST 385: Advanced Bayesian Analysis

• 3 credits, MCW
• Prerequisite: Introduction to Bayesian Analysis, Advanced Statistics I, Statistical Models and Methods III
• Description: 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.

BIOST 386: Theory of Survival Analysis

• 3 credits, MCW
• Prerequisite: Statistical Inference II
• Description: 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.

BIOST 391: Special Topics in Biostatistics

• 1-3 credits
• Description: 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.

BIOST 399: Doctoral Dissertation

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