The Bland-Altman plot is a very popular approach for analysing data from a method agreement study, which I was teaching students about today. We have measurements of a sample of subjects using one measurement technique or method, and a second measurement on each, taken using a new technique or method. The objective is to see how closely the measurements from the two methods agree. If they are very similar, we could use the new method, which may be cheaper, easier or less invasive to use, rather than the old method. The Bland-Altman plot plots the pairwise differences between the measurements against their average. Sometimes one sees a correlation between the pair-wise differences and averages. What is the interpretation of such a correlation?
Jonathan Bartlett
Interpretation of frequentist confidence intervals and Bayesian credible intervals
This post was prompted by a tweet by Frank Harrell yesterday asking:
In this post I’ll say a little bit about trying to answer Frank’s question, and then a little bit about an alternative question which I posed in response, namely, how does the interpretation change if the interval is a Bayesian credible interval, rather than a frequentist confidence interval.
P-values after multiple imputation using mitools in R
I’ve been using Thomas Lumley’s excellent mitools package in R for applying Rubin’s rules for multiple imputation ever since I wrote the smcfcs package in R. Somebody recently asked me about how they could obtain p-values corresponding to the Rubin’s rules results calculated by the MIcombine function in mitools. In this short post I’ll give some R code to calculate these.