Last year I wrote a post about how in linear regression the mean (and sum) of the residuals always equals zero, and so checking that the overall mean of the residuals is zero tells you nothing about the goodness of fit of the model. Someone asked me recently whether the same is true of logistic regression. The answer is yes for a particular residual definition, as I’ll show in this post.
MMRM vs LME model
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.