missing data

If you fit a model with multiply imputed data, you can still plot the line.

What? If you’re in the know, you know there are three major ways to handle missing data: full-information maximum likelihood, multiple imputation, and one-step full-luxury1 Bayesian imputation. If you’re a frequentist, you only have the first two options.

One-step Bayesian imputation when you have dropout in your RCT

Preamble Suppose you’ve got data from a randomized controlled trial (RCT) where participants received either treatment or control. Further suppose you only collected data at two time points, pre- and post-treatment.