Odds ratios, collapsibility, marginal vs. conditional, GEE vs GLMMs

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.

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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.

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Frequentists should more often consider using Bayesian methods

Recently my colleague Ruth Keogh and I had a paper published: 'Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration' (open access here). The paper compares the popular regression calibration approach for handling covariate measurement error in regression models with a Bayesian approach. The two methods are compared from the frequentist perspective, and one of the arguments we make is that frequentists should more often consider using Bayesian methods.

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Why you shouldn't use propensity score matching

I've just watched a highly thought provoking presentation by Gary King of Harvard, available here https://youtu.be/rBv39pK1iEs, on why propensity score matching should not be used to adjust for confounding in observational studies. The presentation makes great use of graphs to explain the concepts and arguments for some of the issues with propensity score matching.

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