To impute or not: the case of an RCT with baseline and follow-up measurements

Under normal conditions, conducting a randomized clinical trial is challenging. Throw in a pandemic and things like site selection, patient recruitment and patient follow-up can be particularly vexing. In any study, subjects need to be retained long enough so that outcomes can be measured; during a period when there are so many potential disruptions, this can become quite difficult. This issue of loss to follow-up recently came up during a conversation among a group of researchers who were troubleshooting challenges they are all experiencing in their ongoing trials. [Read More]

The case of three MAR mechanisms: when is multiple imputation mandatory?

I thought I’d written about this before, but I searched through my posts and I couldn’t find what I was looking for. If I am repeating myself, my apologies. I explored missing data two years ago, using directed acyclic graphs (DAGs) to help understand the various missing data mechanisms (MAR, MCAR, and MNAR). The DAGs provide insight into when it is appropriate to use observed data to get unbiased estimates of population quantities even though some of the observations are missing information. [Read More]