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Advanced Methods in Biostatistics Vi


East Baltimore
2nd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Class Times:
  • Tu Th,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Vadim Zipunnikov



Reviews key topics in modern applied statistics. Extends the topics of 140.755 to encompass generalized linear mixed effects models (GLMMs) and Double Hierarchical Generalized Linear Models (DHGLM) and introduces semiparametric regression via Generalized Additive Models (GAMs) and GAMs for Location, Scale and Shape (GAMLSS), as well as nonparametric smoothing and functional data analysis. Includes extensions of linear mixed effects to discrete outcomes and 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 packages in R.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Use and extend a comprehensive list of models such as Generalized Linear Mixed Models (GLMMs), Double Hierarchical Generalized Linear Models (DHGLMs), Generalized Additive Models for Location, Scale and Shape (GAMLSS) to account for various forms of clustering and correlation often arising in public health studies
  2. Use modern statistical approaches for flexible modelling heterogeneity and making inference
  3. Introduce nonparametric smoothing models
  4. Describe modern statistical methods for complex datasets including functional data analysis
  5. Apply theoretical concepts to scientific data using R software for modeling clustered and functional data
  6. Improve computational and analytic skills through analysis of simulated data sets
Methods of Assessment:

Homeworks and a final exam

Instructor Consent:

No consent required