To understand different types of missing data.

To be able to make a valid statistical analysis in various missing data situations with the R software.

Content

It is quite common that we do not get all the information we want for our statistical analysis. For example, in medical research a person can refuse to provide certain information they feel sensitive, such as weight, substance abuse, sexual orientation etc. Particularly, missing data in longitudinal studies is more the rule than an exception. Missing data in statistical analysis causes all sorts of problems. For example, the desired statistical method cannot be directly applied; loss of information or the results obtained can be biased if the analysis is not done properly accomplished. The course introduces various missing data mechanisms and their effects on statistical analysis. In addition, it presents and evaluates some of the commonly used methods for statistical analysis with missing data. Also special methods for the analysis of longitudinal data are presented including likelihood-based methods and multiple imputation.

Modes of Study

Course work, exam.

This course is an intermediate study version of MTTS1 Statistical Analysis with Missing Data. The student can only complete one of the two versions.