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.
Confidence intervals for the hazard ratio in RCTs which agree with log rank test
The log rank test is often used to test the hypothesis of equality for the survival functions of two treatment groups in a randomised controlled trial. Alongside this, trials often estimate the hazard ratio (HR) comparing the hazards of failure in the two groups. Typically the HR is estimated by fitting Cox’s proportional hazards model, and a 95% confidence interval is used to indicate the precision of the estimated HR.