Generalised estimating equations (GEEs) and generalised linear mixed models (GLMMs) are two approaches to modelling clustered or longitudinal categorical outcomes. Here I will focus on the common setting of a binary outcome. As is commonly described, the two approaches estimate different effect measures, with GEEs targeting so called marginal effects, and GLMMs targeting conditional or subject specific effects. Understanding the difference between these is potentially quite tricky I think.
‘Monte-Carlo sensitivity analysis’ recommended against
McCandless & Gustafson have just published an interesting paper that is available Early View at Statistics in Medicine. They compare a conventional Bayesian analysis to so called ‘Monte-Carlo sensitivity analysis’ for the problem of assessing sensitivity of an exposure effect to unmeasured confounding.
Handling competing risks in randomized trials
Peter Austin and Jason Fine (of Fine & Gray fame) have just published a nice review article in Statistics in Medicine on handling competing risks in randomized trials. They reviewed RCTs published in four top medical journals in the last three months of 2015. Of the 40 trials found with time to event outcomes, Austin & Gray determined that 31 were potentially susceptible to competing risks.