Biostatistics 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.

BIOSTAT501: Introduction to Biostatistics

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Braun, Thomas
  • Prerequisites: SPH MPH or permission of instructor
  • Description: Statistical methods and principles necessary for understanding and interpreting data used in public health and policy evaluation and formation. Topics include descriptive statistics, graphical data summary, sampling, statistical comparison of groups, correlation, and regression. Students will learn via lecture, group discussions, critical reading of published research, and analysis of data.
  • Syllabus for BIOSTAT501

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

BIOSTAT521: Applied Biostatistics

  • Graduate Level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Zawistowski, Matt
  • Prerequisites: Calculus
  • Description: Fundamental statistical concepts related to the practice of public health: descriptive statistics; probability; sampling; statistical distributions; estimation; hypothesis testing; chi-square tests; simple and multiple linear regression; one-way ANOVA. . Taught at a more advanced mathematical level than Biostat 503. Use of the computer in statistical analysis.
  • Syllabus for BIOSTAT521

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

BIOSTAT523: Statistical Methods in Epidemiology

  • Graduate Level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Zhou, Xiang
  • Prerequisites: EPID501 or EPID601 or EPID600; AND BIOSTAT522
  • Description: Statistical methods commonly used in environmental epidemiology. Emphasis on choosing appropriate statistical methods and subsequent interpretation. Topics include probability, measures of association and risk, sample size calculations, SMR and PMR analysis, logistic regression and survival analysis.
  • Syllabus for BIOSTAT523

BIOSTAT600: Introduction to Biostatistics

  • Graduate Level
  • Fall term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Sanchez, Brisa; Welch, Kathy;
  • Prerequisites: Admission to a degree program in Biostatistics
  • Description: The purpose of this course is to review basic applied statistical concepts and tools and to introduce the SPH computer network and statistical software.
  • Syllabus for BIOSTAT600

BIOSTAT601: Probability and Distribution Theory

  • Graduate Level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Wang, Lu
  • Prerequisites: Three terms of calculus
  • Description: Fundamental probability and distribution theory needed for statistical inference. Probability, discrete and continuous distributions, expectation, generating functions, limit theorems, transformations, sampling theory.
  • Syllabus for BIOSTAT601

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

BIOSTAT605: Intro to SAS Statistical Programming

  • Graduate Level
  • Fall term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff; Welch, Kathy;
  • Prerequisites: One course in introductory statistics; Co-requisite Biostat 601 or equivalent or Perm. Instr
  • Description: This course provides incoming master's students in biostatistics with basic experience in SAS programming for data set creation and manipulation, an introduction to SAS macros, and SAS matrix manipulation.
  • Syllabus for BIOSTAT605

BIOSTAT606: Introduction to Biocomputing

  • Graduate Level
  • Fall term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Boehnke, Michael L; Grant, Barry; Jiang, Hui; Kang, Hyun Min; Kidd, Jeff; Kitzman, Jacob; Mills, Ryan; Sartor, Maureen;
  • Prerequisites: Graduate Standing
  • Description: This short course introduces basic computational environments and tools to graduate students with limited prior experience. It will provide an introduction to UNIX systems, software compilation / installation, cluster job management as well as data formats, management, and visualization. A brief introduction to scripting programming languages will also be presented.
  • Course Goals: Students enrolled in the class will develop skills to accelerate their research in computational research environments. Topics will include an intensive introduction to (a) UNIX systems and software management, (b) data processing and simple programming, (c) data formats and visualization, and (d) software version and cluster control. This training will provide a computational foundation that will allow students to focus on the theoretical and biological aspects of their research.
  • Competencies: After completing this class, students are expected to be able to attain the following competencies: Core Competencies: -Navigate and organize UNIX files and folders -Compile and install software in UNIX environments -Understand basic programming data structures and processes -Create simple scripts to manage and analyze data -Utilize and apply popular file formats to modern large-scale data sets -Apply proper visualization tools and strategies to view data -Utilize software versioning technologies for documenting and organizing software -Utilize high-throughput computing clusters for parallel data processing
  • This course is cross-listed with Biostat 606 = HG 606 = Bioinfo 606.
  • Syllabus for BIOSTAT606

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.

BIOSTAT615: Statistical Computing

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kang, Jian
  • Prerequisites: None
  • Description: A survey of key algorithms for statistical computing and its applications in Biostatistics. The course will cover fundamental computational techniques for dynamic programming, sorting, and searching, as well statistical methods for random number generation, numerical integration, function optimization, Markov-Chain Monte Carlo, and the E-M algorithm. Enables students to understand numerical results produced by a computer and to implement their own statistical methods.
  • Syllabus for BIOSTAT615

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

BIOSTAT619: Clinical Trials

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Murray, Susan
  • Prerequisites: Biostatistics 601 and Biostatistics 651
  • Description: This course is designed for individuals with a strong quantitative background who are interested in the statistical design and analysis aspects of clinical trials and the interface between statistics and policy in this area
  • Syllabus for BIOSTAT619

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

BIOSTAT650: Applied Statistics I: Linear Regression

  • Graduate Level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Banerjee, Mousumi
  • Prerequisites: BIOSTAT601
  • Description: Graphical methods, simple and multiple linear regression; simple, partial and multiple correlation; estimation; hypothesis testing, model building and diagnosis; introduction to nonparametric regression; introduction to smoothing methods (e.g., lowess) The course will include applications to real data.
  • Syllabus for BIOSTAT650

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

BIOSTAT653: Applied Statistics III: Longitudinal Analysis

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Wu, Zhenke
  • Prerequisites: BIOSTAT650 and concurrent enrollment in BIOSTAT651
  • Description: This course provides an overview of statistical models and methodologies for analyzing repeated measures/longitudinal data. The course covers general linear models and linear mixed models for analyzing correlated continuous data, as well as marginal (i.e. GEE), conditional (i.e. generalized linear mixed model) and transition models for analyzing correlated discrete data.
  • Competencies: Students are expected to achieve the following competencies: (1) understand the statistical methods used to analyze correlated and longitudinal data in a variety of settings and with a variety of outcome variables; (2) become well-versed in the application of core statistical techniques in analyzing repeated measures identifying an appropriate design and selecting the statistical methods required to analyze the data; (3) master software (e.g. SAS procedures) to perform longitudinal analyses; (4) develop the knowledge to interpret and communicate the clinical and scientific meaning of the results to both statisticians and clinicians/scientists.
  • Syllabus for BIOSTAT653

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

BIOSTAT666: Statistical Models and Numerical Methods in Human Genetics

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Lee, Seunggeun
  • Prerequisites: Biostat 602 or Perm. Instr.
  • Description: Introduction to current statistical methods used in human genetics. Topics will include sampling designs in human genetics, gene frequency estimation, the coalescent method for simulation of DNA sequences, linkage analysis, tests of association, detection of errors in genetic data, and the multi-factorial model. The course will include a simple overview of genetic data and terminology and will proceed with a review of numerical techniques frequently employed in human genetics.
  • Syllabus for BIOSTAT666

BIOSTAT675: Survival Time Analysis

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Li, Yi
  • Prerequisites: Biostat 602 and Biostat 650
  • Description: Concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow-up. Survival time models, clinical life tables, survival distributions, mathematical and graphical methods for evaluating goodness of fit, comparison of treatment groups, regression models, proportional hazards models, censoring mechanisms.
  • Syllabus for BIOSTAT675

BIOSTAT680: Applications of Stochastic Processes I

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Wen, William
  • Prerequisites: Biostat 601 and Math 450 or equiv
  • Description: Conditional distributions, probability generating functions, convolutions, discrete and continuous parameter, Markov chains, medical and health related applications.
  • Syllabus for BIOSTAT680

BIOSTAT682: Applied Bayesian Inference

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kang, Jian
  • Prerequisites: Biostat 602, Biostat 650 and Biostat 651
  • Description: Introduction to Bayesian Inference. Bayesian large sample inference, relationship with maximum likelihood. Choice of model, including prior distribution. Bayesian approaches to regression generalized linear models, categorical data, and hierarchical models. Empirical Bayes methods. Comparison with frequentist methods. Bayesian computational methods. Assessment of sensitivity to model assumptions. Emphasis on biomedical applications.
  • Syllabus for BIOSTAT682

BIOSTAT685: Elements of Nonparametric Statistics

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Zhang, Min
  • Prerequisites: Biostat 602 or STAT 511, and Biostat 650 or Perm. Instr
  • Description: First half covers theory and applications of rank and randomization tests: sampling and randomization models, randomization t-test, Wilcoxon rank sum and signed rank tests, Kruskal-Wallis test, asymptotic result under randomization, relative efficiency; second half covers theory and applications of nonparametric regression: smoothing methods, including kernel estimators, local linear regression, smoothing splines, and regression splines, methods for choosing the smoothing parameter, including unbiased risk estimation and cross-validation, introduction to additive models.
  • Syllabus for BIOSTAT685

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

BIOSTAT698: Modern Statistical Methods in Epidemiologic Studies

  • Graduate Level
  • Fall term(s)
  • 4 Credit Hour(s)
  • Instructor(s): Berrocal, Veronica; Park, Sung Kyun;
  • Not offered 2018-2019
  • Prerequisites: EPID600, BIOSTAT522 and BIOSTAT523 for epid students; BIOSTAT650, BIOSTAT651 for biostat students
  • Description: The goal of this pilot course is to create an interdisciplinary educational experience for Ph.D. students in Epidemiology (also available as an optional elective for Masters students in Biostatistics) through a uniquely designed course that contains lectures on advanced biostatistical methods, but places them in the context of epidemiological applications.
  • Course Goals: Students enrolled in the class will learn about cutting edge statistical methods in these four contemporary topics that arise frequently in the present scientific context. These four topics are: (a) Modern techniques for model building and variable selection; (b) Methods for analyzing longitudinal data; (c) Spatial regression methods; (d) Methods for studies of interaction/effect modification. The course will equip the new generation epidemiologists with state-of-the-art statistical methods in these domains, and teach them the craft of translating a practical problem into mathematical equations. However, the entire theoretical learning process will be placed in the context of sophisticated modeling of data from large complex studies with a focused problem to solve. Data for the projects will come from two studies that Professors Park and Mendes de Leon are involved with: the Normative Aging Study (NAS) and the Chicago Health and Aging Project (CHAP).
  • Competencies: After completing this class, students are expected to be able to attain the following competencies: Core Competencies: -Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met. -Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions. -Apply descriptive techniques commonly used to summarize public health data. -Apply common statistical methods for inference. -Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question. -Interpret results of statistical analyses found in public health studies. Biostatistics Competencies: -Develop knowledge to communicate and collaborate effectively with scientists in a variety of health-related disciplines to which biostatistics are applied (e.g. public health, medicine, genetics, biology; psychology; economics; management and policy). -Become well-versed in the application of core statistical techniques (biostatistical inference, linear regression, generalized linear models, nonparametric statistical methods, linear mixed models) and 4-5 selected statistical specialization techniques. -Select appropriate techniques and apply them to the processing of data from health studies. -Interpret the results of statistical analysis and convert them into a language understandable to the broad statistical community. -Develop written and oral presentation skills and other scientific reporting skills, based on statistical analyses for public health, medical and basic scientists and educated lay audiences Epidemiology Competencies: -Employ state-of-the-art statistical and other quantitative methods in the analysis of epidemiologic data. -Demonstrate a thorough understanding of causal inference, sources of bias, and methods to improve the validity of epidemiologic studies. -Understand the principles and methods of data-collection and data-processing procedures in the design and conduct of epidemiologic research, with sound knowledge of measurement validity and reliability, data quality control, data management, documentation, and security
  • This course is cross-listed with EPID815.
  • Syllabus for BIOSTAT698

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.

BIOSTAT801: Advanced Inference I

  • Graduate Level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Tsodikov, Alexander
  • Prerequisites: Biostat 601, Biostat 602, and MATH 451 or equivalent
  • Description: This is the first course of the sequence that 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.
  • Syllabus for BIOSTAT801

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.

BIOSTAT803: Biostatistics in Cancer Seminar

  • Graduate Level
  • Fall term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Taylor, Jeremy
  • Prerequisites: Perm. Instr.
  • Description: The purpose of this class is to describe biostatistical research that is occuring in collaboration with cancer researchers, and to provide exposure to the field of cancer research. Activities inlcude seminars on statistical methods in cancer; presentations of cancer research; presentations of articles from statistical literature; discussion of cancer clinical tiral protocals and grant proposals; and visits to research laboratories. Students formally in the training program are expected to enroll in this course every semester. The course is open to students not participating in the training grant. It is open to both PhD and Masters students.
  • Syllabus for BIOSTAT803

BIOSTAT810: Approaches to the Responsible Practice of Biostatistics

  • Graduate Level
  • Fall term(s)
  • 1 Credit Hour(s)
  • Instructor(s): Staff
  • Prerequisites: None
  • Description: This course will cover a series of topics that encompass Responsible Conduct of Research and Scholarship (RCRS) as defined by the National Institutes of Health (NIH), as well as focus upon the written and oral communication skills necessary for effective collaboration with public health investigators.
  • Course Goals: (1)To cover the following topics of Responsible Conduct of Research and Scholarship (RCRS): a)Research and Academic Misconduct - Fraud and Financial g) Research and Scholarship in Society and in the Global Workplace (2)To help students develop oral and written communication skills necessary for interaction with non-quantitative audiences. (3)To help students develop oral and written communication skills necessary for interaction with quantitative audiences.
  • Competencies: Core Competencies:(a)Demonstrate effective written and oral skills for communicating with different audiences in the context of professional public health activities; (b)Articulate an achievable mission"" and vision; (c)Demonstrate team building" negotiation" and conflict-management skills; (d)Appreciate the importance of working collaboratively with diverse communities and constituencies (e.g." researchers practitioners agencies" and organizations). Biostatistics Competencies:(a)Describe the roles biostatistics serves in the discipline of public health;(b)Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences;(c)Interpret results of statistical analyses found in public health studies.
  • Syllabus for BIOSTAT810

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.