Missing-Data

My new post demonstrating how to do Bayesian analysis with MI is live.

In Chapter 3 of van Buuren’s Flexible Imputation of Missing Data a variety of methods for imputing univariate missing data are presented. This post will summarize these techniques and show how to implement them in SAS. ...

I’m excited to announce that the new SAPy v4.6.0 release includes a pull request of mine that adds PROC MI to the SAS/STAT procedures directly exposed in SASPy. This procedure allows you to analyze missing data patterns and create imputations for missing data. ...

Understanding whether a variable’s missingness from a dataset is related to the underlying value of the data is a key concept in the field of missing data analysis. We distinguish three broad categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In his book Statistical Rethinking, McElreath1 gives an amusing example to illustrate this concept: he considers variants of a dog eating homework and how the dog chooses - if at all - to eat the homework. The examples he give show substantial shifts in observed values, which make for a good illustration of the types of problems you might encounter. A lecture corresponding to the example from the book can be found on YouTube. In this post, I will first briefly review the different missing data mechanisms before implementing McElreath’s examples in SAS. ...