Prerequisites: Multivariate Calculus (Calculus III) or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and limiting distributions.

Prerequisites: BIOS 510 or permission of instructor. Fundamental concepts in statistical inference will be covered including: statistical models, sampling distributions, standard errors, mean square errors, method of moments, maximum likelihood estimation, asymptotic normality, confidence intervals, hypothesis tests, Wald tests, likelihood ratio tests, power analysis, p-values, multiple comparisons. Common frameworks for inference will be discussed including: parametric/non-parametric/Bayesian inference, the delta method, bootstrap, permutation tests.

Prerequisites: Multivariate Calculus (Calculus III) and Linear Algebra or permission of instructor. Introduction to Probability, random variables, distributions, conditional distributions, expectations, moment generating functions, order statistics, and convergence concepts.

Prerequisites: BIOS 512 or permission of instructor. Introduces the theory of parameter estimation, interval estimation, and tests of hypotheses. In this course, we emphasize the classical "frequentist" (i.e., Neyman-Pearson-Wald) approach to inference. As time permits, we briefly explore alternative paradigms of inference such as neo-Fisherian, Bayesian, and statistical decision theory.

Prerequisites: BIOS 501 or permission of instructor. This is the overview course for the Bioinformatics, Imaging and Genetics (BIG) concentration in the PhD program of the Department of Biostatistics and Bioinformatics. It aims to introduce students to modern high-dimensional biomedical data, including data in bioinformatics and computational biology, biomedical imaging, and statistical genetics. This course will be co-taught by all BIG core faculty members, with each faculty member giving one or two lectures. The focus of the course will be on the data characteristics, opportunities and challenges for statisticians, as well as current developments and hot areas of the research fields of bioinformatics, biomedical imaging and statistical genetics.

Pre-Requisites: PRS 500D as prerequisite or special permission required to enroll. This course covers fundamental concepts and methods used in data analysis. These include techniques in graphical and numerical descriptive statistics, elementary probability calculation using the normal distribution, point and confidence interval estimation and hypothesis testing for population means and proportions, differences between means and between proportions, and contingency table analyses (including risk ratio and odds ratio). Students will use SAS to perform the statistical analysis. Requirements include weekly homework, weekly quizzes, Midterm and Final Exams, and data analysis project.

Prerequisites: BIOS 500 or BIOS 506 or permission of instructor. This course is intended to not only provide a basic grounding in all aspects of the conduct of clinical trials from the perspective of a biostatistician, but also teach students the state-of-the-art knowledge in clinical trials and help them find clinical trial related jobs in pharmaceutical companies, hospitals, oncology research institutes, etc.

Topics of this course include generic drug development, new drug development, pre-clinical trial, the state-of-the-art designs for contemporary Phase I, II, and III clinical trials, protocol writing, hypothesis, methods of randomization, blinding, sample size determination, ethics, subject recruitment, data collection, quality control, monitoring outcomes and adverse events, interim analysis, data analysis, issues with data analysis, reporting, interpreting results, and current advances in clinical trials

Prerequisites: BIOS 506, BIOS 507, BIOS 510, and BIOS 511 or permission of instructor. This course provides an introduction to statistical concepts and methods related to the analysis of survival data. Topics include survival functions, hazard rates, types of censoring and truncation, life tables, log-rank tests, Cox regression models, and parametric regression models. The emphasis is on practical implementation of standard methods using SAS or R and interpretation of results.

Prerequisites: BIOS 508, BIOS 509, BIOS 512, and BIOS 513 or permission of instructor. This course aims to develop basic understanding of the analysis of time-to-event data. The concepts to be introduced include survival functions, hazard rates, right censoring, interval censoring, left truncation, and competing risks. Methods of focus are Kaplan-Meier estimates, log-rank tests, Cox proportional hazards regression models, and parametric regression models. Students will learn how to implement standard survival analysis methods using R and appropriately interpret results.

Prerequisites: BIOS 507 or permission of instructor. This course introduces students to regression techniques commonly used in analyzing longitudinal and multilevel data that are frequently encountered in biomedical and public health research. This course draws motivating examples from environmental and social epidemiology, health services research, clinical studies, and behavioral sciences. The course focuses on data analysis and interpretation. Students will gain practical experience using R for statistical computing.