Just use multilevel models for your pre/post RCT data

I’ve been thinking a lot about how to analyze pre/post control group designs, lately. Happily, others have thought a lot about this topic, too. The goal of this post is to introduce the change-score and ANCOVA models, introduce their multilevel-model counterparts, and compare their behavior in a couple quick simulation studies. Spoiler alert: The multilevel variant of the ANCOVA model is the winner.

Example power analysis report, II

In an earlier post, I gave an example of what a power analysis report could look like for a multilevel model. At my day job, I was recently asked for a rush-job power analysis that required a multilevel model of a different kind and it seemed like a good opportunity to share another example.

Use emmeans() to include 95% CIs around your lme4-based fitted lines

You’re an R user and just fit a nice multilevel model to some grouped data and you’d like to showcase the results in a plot. In your plots, it would be ideal to express the model uncertainty with 95% interval bands. If you’re a frequentist and like using the popular lme4 package, you might be surprised how difficult it is to get those 95% intervals. I recently stumbled upon a solution with the emmeans package, and the purpose of this blog post is to show you how it works.