Missing data arise in many fields of research, and a large body of statistical tools has been developed to facilitate statistical analysis in the presence of missing data.  Here we focus mainly on multiple imputation, which is a broadly-applicable approach for working with missing data. We will illustrate through several case studies how multiple imputation allows certain types of missing data to be rigorously accounted for, while preserving the flexibility to use a variety of familiar statistical tools to account for other aspects of the data.   

The analyses presented in this workshop will be performed in Python using the Statsmodels package. All software tools covered in this workshop are free and open source.