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.
Jonathan Bartlett
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.
Mixed model repeated measures (MMRM) in Stata, SAS and R
Linear mixed models are a popular modelling approach for longitudinal or repeated measures data. They extend standard linear regression models through the introduction of random effects and/or correlated residual errors. In the context of randomised trials which repeatedly measure patients over time, linear mixed models are a popular approach of analysis, not least because they handle missing data in the outcome ‘automatically’, under the missing at random assumption. Because of this a mixed model analysis has in many cases become the default method of analysis in clinical trials with a repeatedly measured outcome.