Course: Multiple imputation techniques for working with missing data

Missing data is a common problem in many studies and is particularly prominent in large-scale studies and/or in studies involving repeated measurements over time. This course covers the general statistical techniques and methods based on multiple imputation using fully conditional model specification that are suitable for analyzing and obtaining unbiased results even missing data are a problem.
The course will contain equal parts theory and applications and consists of two full days of teaching and computer lab exercises. It is the intention that the participants will have a thorough understanding of the missing data mechanisms, their impact on the analyses, and how and when to use multiple imputation to alleviate the problems. Similarly, the students should be able to apply these methods practice after having followed the course. This course is aimed at health researchers with previous knowledge of statistics and the computer language R who need of an overview about appropriate analytical methods and discussions with statisticians to be able to solve their problem.

Dates: September 16th-17th

Teacher: Jonathan Bartlett, University of Bath

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