Mohan and Pearl have just had published a paper ‘Graphical Models for Processing Missing Data’ (open access pre-print here, journal version here). It’s a great read, and no doubt contains lots of useful developments (I’m still working my way through the paper). But something strikes me as somewhat troubling about their missing at random definition. Years ago when working with colleagues on using directed acyclic graphs to encode missing data assumptions, we struggled to see how MAR monotone dropout, as might occur in a longitudinal study, could be encoded in a DAG. In this post I will try and see whether MAR monotone dropout is classified as MAR according the definitions of Mohan and Pearl.
missing at random dropout
Understanding missing at random dropout using DAGs
I previously wrote a post about the meaning of missing at random for longitudinal data in clinical trials, stemming from an earlier question from someone. Somebody recently asked an excellent question in the comments to this post, which here I’ll follow-up on using directed acyclic graphs (DAGs). The idea of using DAGs to under missingness assumptions has been written about by a number of authors, including Daniel et al and Thoemmes and Mohan.