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.
Prediction intervals after random-effects meta-analysis
Christopher Partlett and Richard Riley have just published an interesting paper in Statistics in Medicine (open access here). They examine the performance of 95% confidence intervals for the mean effect and 95% prediction intervals for a new effect in random-effects meta-analysis.
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.