ADVANCED METHODS IN BIOSTATISTICS VI Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Give examples of different types of data arising in public health studies
- Use modern statistical concepts such as Generalized Linear Mixed Models (GLMMs) for inference
- Describe the relationship between GLMMs and linear mixed models
- Extend models to account for clustering and correlation
- Introduce nonparametric smoothing models
- Describe modern statistical methods for complex datasets including functional data analysis
- Apply theoretical concepts to scientific data using R software
- Improve computational and analytic skills through analysis of simulated data sets
Reviews key topics in modern applied statistics. Extends the topics of 140.755 to encompass generalized linear mixed effects models (GLMMs) and introduces semiparametric regression, nonparametric smoothing, and functional data analysis. Includes extensions of linear mixed effects to discrete outcomes, nonlinear and multivariate smoothing, semi-parametric models for clustered data. Emphasizes both rigorous methodological development and practical data analytic strategies. Presents computational methods designed for semi-parametric inference and discusses relevant software.
Intended AudienceBiostatistics PhD students
Methods of Assessment
Homework and a final project
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
The course will cover the following topics:
1. Generalized Linear Mixed Models
2. Laplace approximation
3. Penalized Quasi-Likelihood
4. Expectation-Maximization (EM) and Majorize-Minimization (MM) algorithms
5. Importance Sampling
6. Quasi Monte Carlo (Quasi MC)
8. Markov Chain Monte Carlo (Metropolis-Hastings and Gibbs sampling)
9. Functional Data Analysis
Disability Support ServicesIf you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services: firstname.lastname@example.org, 410-955-3034, or 2017 E. Monument Street.