In an earlier post, we walked through method for plotting the fitted lines from models fit with multiply-imputed data. In this post, we’ll discuss another neglected topic: How might one compute standardized regression coefficients from models fit with multiply-imputed data?
If you’re an R user and like multiple imputation for missing data, you probably know all about the mice package. The bummer is there are no built-in ways to plot the fitted lines from models fit from multiply-imputed data using van Buuren’s mice-oriented workflow. However, there is a way to plot your fitted lines by hand and in this blog post I’ll show you how.
Say you have 2-timepoint RCT, where participants received either treatment or control. Even in the best of scenarios, you’ll probably have some dropout in those post-treatment data. To get the full benefit of your data, you can use one-step Bayesian imputation when you compute your effect sizes. In this post, I’ll show you how.