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Courses

Biostatistics
  • BIOS-S 634 Stochastic Modeling in Biomedical and Health Sciences (pending approval) (3 cr.) P: STAT 52800. The aim of this course is to develop those aspects of stochastic processes that are relevant for modeling important problems in health sciences. Among the topics to be covered are: Poisson processes, birth and death processes, Markov chains and processes, semi-Markov processes, modeling by stochastic diffusions. Applications will be made to models of prevalence and incidence of disease, therapeutic clinical trials, clinical trials for prevention of disease, length biased sampling, models for early detection of disease, cell kinetics and family history problems.
  • BIOS-S 515 Biostatistical Practicum (1-3 cr.) P: STAT 52100; BIOS-S 527, BIOS-S 546; or consent of instructor. Real-world projects in biostatistics involving participation in consulting sessions, directed reading in the literature, research ethics, design of experiments, collection of data and applications of biostatistical methods. Detailed written and oral reports required. May be repeated, up to 6 credits.
  • BIOS-S 527 Introduction to Clinical Trials (3 cr.) P: STAT 51200, exposure to survival analysis; or consent of instructor. Prepares biostatisticians for support of clinical trial projects. Topics: fundamental aspects of the appropriate design and conduct of medical experiments involving human subjects including ethics, design, sample size calculation, randomization, monitoring, data collection analysis and reporting of the results.
  • BIOS-S 530 Statistical Methods in Bioinformatics (pending approval) (3 cr.) P: STAT 51200, STAT 51900; or consent of instructor. Covers a broad range of statistical methods used in many areas of bioinformatics research, including sequence alignment, genome sequencing and gene finding, gene expression microarray analysis, transcriptional regulation and sequence motif finding, comparative genomics, and proteomics.
  • BIOS-S 546 Applied Longitudinal Data Analysis (3 cr.) P: STAT 51200, STAT 52500; or permission of instructor. Covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data. Topics: repeated measures ANOVA, random effects and growth curve models, generalized estimating equations (GEE) and generalized linear mixed models (GLMMs). Extensive use of statistical software, e.g. SAS, R.
  • BIOS-S 621 Advanced Statistical Computing (3 cr.) P: STAT 52100, STAT 52500, STAT 52800. A study of computing methods commonly used in statistics. Topics include computer arithmetic, matrix algebra, numerical optimization methods with application to maximum likelihood estimation and GEEs, spline smoothing and penalized likelihood, numerical integration, random number generation and simulation methods, Gibbs sampling, bootstrap methods, missing data problems and EM, imputation, data augmentation algorithms, and Fourier transforms. Students should be proficient with effective implementation of numerical algorithms in one of commonly used computer languages (C, Fortran, S, R or similar).
  • BIOS-S 627 Statistics in Pharmaceutical Research (3 cr.) P: STAT 51200; BIOS-S 527, BIOS-S 546. An overview of the drug development process, including the various phases of development from pre-clinical to post-marketing. Topics: statistical issues in design, study monitoring, analysis and reporting. Additional topics may include regulatory and statistical aspects of population pharmacokinetics and real world applications.
  • BIOS-S 636 Advanced Survival Analysis (3 cr.) P: STAT 62800. Discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods. Material is drawn from recent literature.
  • BIOS-S 646 Advanced Generalized Linear Models (3 cr.) P: BIOS-S 546. Presents classical and modern approaches to the analysis of multivariate observations, repeated measures, and longitudinal data. Topics include the multivariate normal distribution, Hotelling's T2, MANOVA, the multivariate linear model, random effects and growth curve models, generalized estimating equations, statistical analysis of multivariate categorical outcomes, and estimation with missing data. Discusses computational issues for both traditional and new methodologies.
  • BIOS-S 612 Modern Statistical Learning Methods (3 cr.) P: STAT 52500. This course covers the various topics pertaining to the modern methods of high-dimensional data analysis. Course is still subject to final approval by The University Graduate School.
  • BIOS-S 698 Topics in Biostatistical Methods (1-3 cr.) P: Consent of instructor. Directed study and reports for students who wish to undertake individual reading and study on approved topics.
  • BIOS-S 699 Ph.D. Thesis/Research (1-15 cr.) P: Must have been admitted to candidacy. See advisor for more information. Research required by the graduate students for the sole purpose of writing a Ph.D. Dissertation.
  • BIOS-S 634 Stochastic Modeling in Biomedical and Health Sciences (3 cr.) P: STAT 52800. The aim of this course is to develop those aspects of stochastic processes that are relevant for modeling important problems in health sciences. Among the topics to be covered are: Poisson processes, birth and death processes, Markov chains and processes, semi-Markov processes, modeling by stochastic diffusions. Applications will be made to models of prevalence and incidence of disease, therapeutic clinical trials, clinical trials for prevention of disease, length biased sampling, models for early detection of disease, cell kinetics and family history problems.