This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package as well as more fundamental and flexible Bayesian approaches.
The course is run over 3 days with 8 lecture classes, 3 guided practicals, and 3 self-guided practicals. The topics covered include:
- An introduction to missing data analysis, and some common methods with examples
- Introduction to Bayesian analysis and missing data
- The use of Bayesian and likelihood-based methods in missing data analysis
- The fully conditional specification approach to missing data analysis
- An introduction to the mice package
- Bayesian software tools JAGS/Stan for missing data analysis
- More advanced missing data analysis including non-ignorable and not missing at random methods
- Missing data analysis in machine learning
The practical sessions cover:
- How to run a missing data analysis in mice
- Including missing data in JAGS and Stan
- Advanced missing data analysis methods
Research postgraduates, practicing academics, or other professionals from any field who would like to learn about missing data analysis and how it can help them produce better quality information from their data.
Participants should have :
- A basic understanding of regression methods and generalised linear models.
- Some familiarity with R including the ability to import/export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.
- A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and Mac and can be downloaded for free by following these links: R, Rstudio.
You can start browsing the course by visiting the timetable