BMI Department Courses

  • BMI 511 Introduction to Biostatistics

    Fall - 3 credits

    This course will provide a breadth in biostatistical methods for public health practitioners. Topics will include research design, data collection methods and database management, statistical computing and programming, descriptive statistics in tables and graphics, and biostatistical methods for summary measures, probability and distributions, sampling distributions, statistical inference, hypothesis testing and statistical comparison, nonparametrics, correlation, regression analysis and survey sampling.

  • BMI 541 Introduction to Biostatistics

    Fall - 3 credits

    Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic. Prerequisite: Math 221 or equivalent or instructor's consent.

  • BMI 542 Introduction to Clinical Trials

    Spring - 3 credits

    Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. Statistics graduate students should take Stat 641. Prerequisite: Stat 541 or equivalent or instructor's consent. Previous course materials

  • BMI 544 Introduction to Clinical Trials ll

    Fall - 3 credits

    This course will provide practical experience and training in clinical trial research. The course will focus on the design, implementation, and conduct of clinical trials. Topics include: regulatory requirements for cinical trials; data collection strategies, data quality and management; budget development and justification; federal, institutional, and sponsor-defined requirements; establishment of research infrastructures for safety and success; preparation of investigator-INDs; investigator responsibilities in Phase l-IV trials. Development of data collection and data management systems and a budget for the protocol developed in 541 are require components of this course.

  • BMI 546 Practicum in Clinical Trial Data Analysis and Interpretation

    as needed - 3 credits

    This course will provide practice in analysis and interpretation of existing datasets from national and international clinical trials in a variety of diseases. Students will develop a research question, review clinical protocols, and analyze available data to prepare a report. Prerequisites: Stat 541 or Stat 572 and Stat 542 or Stat 641.

  • BMI 551 Introduction to Biostatistics for Population Health (cross-listed with Population Health Sciences)

    Fall - 3

    The course provides research-oriented students in the population health program with a thorough grounding in basic probability and statistics. An understanding of the procedures and applications to population health problems are stressed. The following topics are covered: descriptive statistics and graphical methods, elementary probability, elementary properties of random variables, binomial distribution, Poisson distribution, normal distribution, Central Limit Theorem, normal approximations to the binomial and Poisson, one-sample inference for the normal mean and variance, one-sample inference for the binomial and Poisson, paired t-test, two-sample t-test, two-sample tests for binomial data, measures of effect for binomial data (odds ratio, relative risk, risk difference) and power and sample size calculations.

  • BMI 576 Introduction to Bioinformatics

    3 credits, cross-listed as Computer Sciences 576
    The goals of this course are to provide an understanding of the fundamental computational problems in molecular biology and a core set of widely used algorithms. This is the first of two courses on bioinformatics. The topics it will cover include: pairwise sequence alignment, multiple sequence alignment, finding genes in DNA sequences, phylogenetic tree construction, and genome mapping and sequencing. This is currently being taught as a special topics course in Computer Sciences. Prerequisites: Math 222 and Computer Sciences 367.

  • BMI 641 Statistical Methods for Clinical Trials

    Fall - 3 credits

    This course covers statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring. It is intended for statistics graduate students; those with medical backgrounds should take Stat 542.

    Prerequisites: Math/Stat 310 or equiv or instructor consent

  • BMI 642 Statistical Methods for Epidemiology

    Spring - 3 credits

    This course covers methods for analysis of case-control, cross sectional, and cohort studies. Epidemiological study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression are also covered.

    Prerequisites: Statistics 310 or equivalent or instructor consent

  • BMI 776 Advanced Bioinformatics

    Spring - 3 credits
    The goals of this course are to provide an understanding of the fundamental computationalproblems in molecular biology and a core set of widely used algorithms. This is the second of two courses on bioinformatics. The topics it will cover include: probabilistic methods for sequence modeling, gene expression analysis, phylogenetic tree construction, protein structure prediction, RNA modeling, whole-genome analysis, and algorithms for exploiting biomedical text sources. A precursor to this course was taught as a special topics course in Computer Sciences in the Fall 1999 semester. Prerequisites: Computer Sciences 576.

  • BMI 918 Health Informatics for Medical Students

    Fall, Spring - 2 credits

    (cross-listed with Department of Medicine)
    Explore medical informatics as a new way to practice medicine with applications to patient care, electronic medical records, and patient safety. Prerequisites: College algebra; enrolled in Population Health MS or PhD program, consent of instructor.

  • BMI/CS 767 Computational Methods for Medical Image Analysis

    Spring - 3 credits

    Study of computational methods that facilitate automated analysis, manipulation, denoising, and improvement of large-scale and high resolution medical images. Design and implementation of methods from Computer Vision and Machine Learning to efficiently process such image data to answer biologically and clinically meaningful scientific questions.

  • BMI/Stat 768 Statistical Methods for Medical Image Analysis

    Fall - 3 credits

    BMI 210-768/STAT 932-768 Statistical Methods for Medical Image Analysis
    The course is designed for graduate students, and researchers who wish to learn quantitative techniques in analyzing medical images and set up statistical models. The course material is applicable to a wide variety of statistical problems in medical and biological images. Present statistical and other quantitative techniques used in analyzing various medical images. A concise review of relevant methodological background will be presented. Basic concepts of key methods will be developed with considerable attention to analysis of real medical maging data of various types and problems. Students and researchers should gain a deeper understanding of statistical methods used in medical image analysis. Course projects will be designed to apply methods learned from classes to medical images provided by an instructor or students themselves. No prerequisites.

  • BMI/Stat 877 Statistical Methods for Molecular Biology

    Spring 3 credits (cross-listed with Department of Statistics)

    The course will provide a statistical perspective on some current biological problems, with an introduction to statistical analysis in genomics, phylogenetics, gene regulation, gene expression, gene mapping by linkage or association, and related areas. Statistical concepts will include: stochastic modeling, hierarchical modeling, likelihood methods, Bayesian methods, multivariate analysis methods, model selection, high-dimensional parameters, experimental design strategies, and multiple testing. Biological concepts will include: microarray and related measurement of DNA, RNA, and protein; genomic resources; the relationship between genotype and phenotype; breeding designs; pedigrees; and phylogenies. Prerequisites: Stat 309-310 or 609-610 or 709-710 or equivalent, or consent of instructor, also Genetics 466 or equivalent strongly recommended. The course is team taught- this semester the course leader is Sunduz Keles.