QBS 122 Foundations of Biostatistics III: Modeling Complex Data
This course follows QBS 120 (Biostatistics I: Theoretical Foundations) and QBS 121 (Biostatistics II: Modeling). The first module of the course extends standard regression models to analyze data when the data are statistically dependent. This component encompasses clustered, multi-level, longitudinal and other forms of structured data and will focus on hierarchical (mixed-effect) modeling approaches. The consideration of random effects and their conditional distribution given that data links to the final two modules. Bayesian methodology is carefully developed and compared to the classical (frequentist) approach. Bayesian statistical methods are a feature of this course due to their affinity for solving challenging problems and their ubiquity across modern statistical applications. A variety of applications in which Bayesian methods are naturally suited are considered. Bayesian computation via Markov-chain Monte- Carlo (MCMC) is also developed and illustrated. The course concludes with the network analysis module. This includes visualization and summarization of networks; models of networks; and models of peer effects and social influence processes. The techniques and methods developed in the two prior modules are further illustrated in this module.
Learning Objectives
- Become adept at recognizing when data has a nested, cross-classified, longitudinal or multivariate structure and familiar with statistical techniques for analyzing such data
- Gain a strong understanding of the fundamentals of Bayesian Analysis and develop an ability to perform Bayesian analyses
- Be able to conduct a social network analysis from the grassroots, encompassing specification of the research question, representation of data, choice of statistical analysis, implementation of analysis and visualization of results
(1 unit)
Instructor
Dr. James O'Malley
Prerequisite
QBS 119 and permission of instructor, or
QBS 120.
QBS 121. The course has a strong “hands-on” emphasis on analyzing data while consolidating ideas through relevant methodological and intuitive insights. Linear algebra, multivariate calculus, statistics, probability and basic computer programming with an emphasis on mathematical/statistical programming. Programming: Intermediate proficiency in R.