Biostatistics Winter Term Courses

BIOSTAT449 Topics In Biostatistics

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: Statistics 401 or permission of instructor
  • Description: This course will make use of case studies to discuss problems and applications of biostatistics. Topics will include cohort and case control studies, survival analysis with applications in clinical trials, evaluation of diagnostic tests, and statistical genetics. The course will conclude with a survey of areas of current biostatistical research.
  • This course is cross-listed with Statistics 449 in the Literature, Science and the Arts department.

BIOSTAT502 Application of Regression Analysis to Public Health Studies

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Han, Peisong
  • Prerequisites: Biostat 501, 521 or Perm. Instr.
  • Description: Biostat 502 will cover a general overview of linear, logistic, Poisson, and Cox regression. The course will use SPSS as the statistical software.

BIOSTAT512 Analyzing Longitudinal and Clustered Data Using Statistical Software

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Welch, Kathy
  • Prerequisites: BIOSTAT 501 or 521
  • Description: Longitudinal data sets occur often in a Public Health setting. This course will introduce students to methods for analyzing both clustered and longitudinal data using the statistical software packages SAS and Stata. Models for both continuous and discrete (e.g., binary, count) outcomes will be discussed and illustrated. The course will have one session of lecture and one session of lab per week. The course will be driven primarily by using both software packages to analyze real data sets.
  • Syllabus for BIOSTAT512

BIOSTAT522 Biostatistical Analysis for Health-Related Studies

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kim, Myra
  • Prerequisites: BIOSTAT521; BIOSTAT501 w/ instructors permission.
  • Description: A second course in applied biostatistical methods and data analysis. Concepts of data analysis and experimental design for health-related studies. Emphasis on categorical data analysis, multiple regression, analysis of variance and covariance.
  • Syllabus for BIOSTAT522

BIOSTAT602 Biostatistical Inference

  • Graduate Level
  • Winter term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Sen, Ananda
  • Prerequisites: Biostat 601
  • Description: Fundamental theory that is the basis of inferential statistical procedures. Point and interval estimation, sufficient statistics, hypothesis testing, maximum likelihood estimates, confidence intervals, criteria for estimators, methods of constructing test and estimation procedures.
  • Syllabus for BIOSTAT602

BIOSTAT610 Readings in Biostatistics

  • Graduate Level
  • Fall, Winter term(s)
  • 1-4 Credit Hour(s)
  • Instructor(s):
  • Prerequisites: One of Biostat 503, Biostat 524, Biostat 553 or Biostat 601/Biostat 602
  • Description: Independent study in a special topic under the guidance of a faculty member. May be elected more than once. Enrollment is limited to biostatistics majors.

BIOSTAT617 Theory and Methods of Sample Design (Soc 717 and Stat 580 and SurvMeth 617)

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Elliot, Michael
  • Prerequisites: Three or more courses in statistics, and preferably a course in methods of survey sampling
  • Description: Theory underlying sample designs and estimation procedures commonly used in survey practice.
  • This course is cross-listed with Stats 580 Soc 717 SurvMeth617 in the Rackham department.
  • Syllabus for BIOSTAT617

BIOSTAT646 High Throughput Molecular Genetic and Epigenetic Data Analysis

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Sartor, Maureen; Tsoi, Alex;
  • Prerequisites: Graduate Standing and STAT400, BIOSTAT522, or BIOSTAT521 or permission of instructor
  • Description: The course will cover statistical methods used to analyze data in experimental molecular biology. The course will primarily cover topics relating to gene expression data analysis, but other types of data such as genome sequence and epigenomics data that is sometimes analyzed in concert with expression data will also be covered.
  • Syllabus for BIOSTAT646

BIOSTAT651 Applied Statistics II: Extensions for Linear Regression

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): LI, Yun
  • Prerequisites: BIOSTAT601 and BIOSTAT650
  • Description: Introduction to maximum likelihood estimation; exponential family; proportion, count and rate data; generalized linear models; link function; logistic and Poisson regression; estimation; inference; deviance; diagnosis. The course will include application to real data.
  • Syllabus for BIOSTAT651

BIOSTAT664 Special Topics in Biostastics

  • Graduate Level
  • Winter term(s)
  • 1-4 Credit Hour(s)
  • Instructor(s): Wen, William
  • Prerequisites: Permission of instructor
  • Description: Master's level seminar designed to provide an extensive review of a number of substantive and methods and skill areas in biostatistics. Readings, discussion, and assignments are organized around issues of mutual interest to faculty and students. Reviews and reports on topics required in the areas selected. May be elected more than once.
  • Syllabus for BIOSTAT664

BIOSTAT665 Statistical Population Genetics

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Zoellner, Sebastian
  • Not offered 2018-2019
  • Description: The first half of the course concentrates on classical population genetics. We introduce topics such as Hardy-Weinberg equilibrium, models of selection for populations of infinite size and population subdivision. The second half of the course focuses on coalescent theory, covering migration, changes in population size and recombination. We provide guidelines how these models can be used in to infer population genetic parameters. Finally, some recent results and methods from the population genetic literature are discussed.
  • Syllabus for BIOSTAT665

BIOSTAT695 Analysis of Categorical Data

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Johnson, Timothy
  • Prerequisites: Biostat 602 and Biostat 660
  • Description: Regression models for the analysis of categorical data: logistic, probit and complementary log-log models for binomial random variables; log-linear models for cross-classifications of counts; regression models for Poisson rates; and multinomial response models for both nominal and ordinal responses. Model specification and interpretation are emphasized, and model criticism, model selection, and statistical inference are cast within the framework of likelihood based inference.
  • Syllabus for BIOSTAT695

BIOSTAT696 Spatial Statistics

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Berrocal, Veronica
  • Prerequisites: BIOSTAT 601, BIOSTAT 602, BIOSTAT 650, BIOSTAT 653
  • Description: This course will introduce the theory and methods of spatial and spatio-temporal statistics. It will present spatial and spatio-temporal statistical models and will discuss methods for inference on spatial processes within a geostatistical and a hierarchical Bayesian framework.
  • Syllabus for BIOSTAT696

BIOSTAT699 Analysis of Biostatistical Investigations

  • Graduate Level
  • Winter term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Taylor, Jeremy; Sanchez, Brisa; Braun, Thomas;
  • Prerequisites: Registration for last term of studies to complete MS or MPH
  • Description: Identifying and solving design and data analysis problems using a wide range of biostatistical methods. Written and oral reports on intermediate and final results of case studies required.
  • Syllabus for BIOSTAT699

BIOSTAT800 Seminar in Biostatistics

  • Graduate Level
  • Fall, Winter term(s)
  • 0.5 Credit Hour(s)
  • Instructor(s): Han, Peisong
  • Prerequisites: Graduate level Biostatistics students only
  • Description: Presentations and discussions of current consulting and research problems. Enrollment limited to biostatistics majors. Students must attend 2/3 of all seminars offered during the semester to receive credit. Maximum credit is 0.5 per semester. No more than 1 credit total allowed. May only be taken a maximum of 2 semesters.

BIOSTAT802 ADVANCED INFERENCE II

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Sen, Ananda
  • Prerequisites: Biostat 601, Biostat 602, and MATH 451 or equivalent
  • Description: This sequence covers advanced topics in probability theory, theory of point estimation, theory of hypothesis testing, and related large sample theory. This sequence replaces STAT 610/611 as biostatistics Ph.D. requirements.
  • Course Goals: The goal of the sequence is to provide broad and deep theoretical training to Biostatistics Ph.D. students. Such training is essential for success in their thesis research and their future career.
  • Competencies: The following competencies under Appendix 2.6.c in ``University of Michigan School of Public Health Self-Study -- Appendices" for Biostatistics PhD students are met: 2. Statistical techniques a. Advanced Mathematical Statistics b. Generalized Linear and Mixed Models c. Advanced Biostatistical Inference d. Stochastic Processes j. Bioinformatics and analysis of high-throughput biological data k. Survival analysis m. Bayesian inference techniques n. Nonparametric statistical methods 3. Mathematical foundation The graduate must acquire mathematical proficiency to be able to pursue theoretical development of statistical methods to address the needs of Biostatistical Inference.

BIOSTAT815 Advanced Topics in Computational Statistics

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kang, Hyun Min
  • Prerequisites: BIOSTAT601, BIOSTAT602 and BIOSTAT615 or equiv and proficiency in C++ and R
  • Description: Modern numerical analysis for statisticians. Combination of theory and practical computational examples illustrating the current trends in numerical analysis relevant to probability and statistics. Topics choose from numerical linear algebra, optimization theory, quadrature methods, splines, and Markov chains. Emphasis on newer techniques such as quasi-random methods of integration, the EM algorithm and its variants, and hidden Markov chains. Applications as time permits to areas such as genetic and medical imaging.
  • Syllabus for BIOSTAT815

BIOSTAT820 Readings in Biostatistics

  • Graduate Level
  • Fall, Winter, Spring-Summer term(s)
  • 1-4 Credit Hour(s)
  • Instructor(s): Staff
  • Description: Students assigned special topics for literature study under guidance of individual faculty members. May be elected more than once. Enrollment limited to biostatistics majors.

BIOSTAT830 Advanced Topics in Biostatistics

  • Graduate Level
  • Fall, Winter term(s)
  • 1-4 Credit Hour(s)
  • Instructor(s): Little, Roderick; Wang, Lu;
  • Prerequisites: course/instructor dependent
  • Description: Advanced training in biostatistical methods primarily for doctoral students. Format will include lectures, readings, presentations and discussions in an area of special interest to students and faculty, such as stopping rules and interim analysis in clinical trials, conditional and unconditional inference and ancillarity, or nonparametric regression.
  • Syllabus for BIOSTAT830

BIOSTAT866 Advanced Topics in Genetic Modeling

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Boehnke, Michael L
  • Prerequisites: Biostat 601, Biostat 602, Biostat 666 or Perm. Instr.
  • Description: Advanced topics in quantitative genetics with emphasis on models for gene mapping, pedigree analysis, reconstruction of evolutionary trees, and molecular genetics experiments, computational mathematics, and statistical techniques such as Chen-Stein Poisson approximations, hidden Markov chains, and the EM algorithm introduced as needed.

BIOSTAT875 Advanced Topics in Survival Analysis

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Murray, Susan
  • Prerequisites: Biostat 675
  • Description: Lectures and readings from the literature on advanced topics in survival analysis. Covers regression for censored data, general event-history data and models, competing risks. Statistical, mathematical, and probabilistic tools used in survival analysis are extended for these general problems.
  • Syllabus for BIOSTAT875

BIOSTAT880 Statistical Analysis With Missing Data

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s):
  • Not offered 2018-2019
  • Prerequisites: Biostat 602 and 651, and at least one of Biostat 690, Biostat 851, Biostat 890, or Biostat 895 or Perm Inst.
  • Description: Statistical analysis of data sets with missing values. Pros and cons of standard methods such as complete-case analysis, imputation. Likelihood-based inference for common statistical problems, including regression, repeated-measures analysis, and contingency table analysis. Stochastic censoring models for nonrandom nonresponse. Computational tools include the EM algorithm, the Gibbs’ sampler, and multiple imputation.
  • Syllabus for BIOSTAT880

BIOSTAT885 Nonparametric Statistics

  • Graduate Level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Song, Peter Xuekun
  • Not offered 2018-2019
  • Prerequisites: Biostat 601/602 or Perm. Instr.
  • Description: Theory and techniques of nonparametrics and robustness. M-estimation, influence function, bootstrap, jackknife, generalized additive models, smoothing techniques, penalty functions, projection pursuit, CART.
  • Syllabus for BIOSTAT885

BIOSTAT990 Dissertation/Pre-Candidacy

  • Graduate Level
  • Fall, Winter, Spring-Summer term(s)
  • 1-8 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: (1-8 Full term, 1-4 Half term)
  • Description: Election for dissertation work by doctoral student not yet admitted to status as a candidate.

BIOSTAT995 Dissertation Research for Doctorate in Philosophy

  • Graduate Level
  • Fall, Winter, Spring-Summer term(s)
  • 1-8 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: Admission to Doctoral Program(1-8 Full term, 1-4 Half term)
  • Description: Election for dissertation work by doctoral student who has been admitted to status as a candidate.