Bayesian modelling approaches provide natural ways for researchers in many disciplines to structure their data and knowledge, and they yield direct and intuitive answers to scientific questions (see https://bayesian.org/Bayes-Explained). In this course, students will learn how to construct Bayesian models to relate (potentially complex) data to scientific questions, to fit such models fitting using statistical programs (R, JAGS and/or STAN), to interpret model results and lastly, to check model assumptions.

Students choose a topic on their own under the guidance of a faculty adviser who will then grade the final written report

This is not available at this time

The goal of this course is to introduce some statistical modeling approaches, which have been developed in the last few years and are widely used in medical and public health research, but are not covered in the core courses of the MS/PhD programs in Biostatistics and Epidemiology. Topics include penalized regression, methods for classification, evaluation of predictions, and robust regression. The cross-validation and bootstrapping procedures, which are important in evaluating and performing inference for models, will be introduced.

The aim of this course is to provide fundamental statistical concepts and tools relevant to the practice of summarizing, analyzing, and visualizing data. Continuing where Introduction to Biostatistics (PUBHLTH 540) left off, this course will build your knowledge of the fundamental principles of biostatistical inference. The course will focus on linear regression and generalized linear regression models. We will use a variety of examples and exercises from medical and public health studies.

Integration of linear models with experimental design and sampling, considering applications with unbalanced and missing data. In-depth discussion of mixed models including parameterizations, analysis of covariance, unequal numbers of observations per cell, missing cells. Repeated measure designs and longitudinal data analysis emphasized, with many examples illustrated using SAS. Prerequisite: BIOST&EP 640 and 691F or equivalent. Recommended: Bioepi 741 and 744.

Principles of statistics applied to analysis of biological and health data. Continuation of Bioepi 540 including analysis of variance, regression, nonparametric statistics, sampling, and categorical data analysis.

Not available at this time.