Following my recent post on fitting an MMRM in SAS, R, and Stata, someone recently asked me about when it is preferable to use a Mixed Model Repeated Measures (MMRM) analysis as opposed to a a linear mixed effects model (LME) which includes subject level random effects (e.g. intercepts).
Is MAR dropout classified as MNAR according to Mohan and Pearl?
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.
Confounding vs. effect modification
A student asked me today about the differences between confounding and effect modification. In this post I’ll try and distinguish these conceptually and illustrate the differences using some very large simple simulated datasets in R.