The smcfcs package in R imputes missing values of covariates compatibly (congenially) with the user’s specified outcome or substantive model. Just like the regular chained equations (fully conditional specification) multiple imputation method, smcfcs is an iterative procedure, and users should check that they have used enough iterations for the process to have (hopefully) converged to its stationary distribution. The smcfcs package returns a matrix with the parameter estimates of the outcome/substantive model from each imputed dataset and iteration within these. But it requires the user to figure out how to appropriately plot these.
What does correlation in a Bland-Altman plot mean?
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?
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.