In clinical trials patients often dropout from the trial, for a variety of reasons. Historically outcome measures were not obtained after such dropout, and the dropout often coincided with the patient no longer receiving their original randomised treatment. For a treatment policy estimand (i.e. what historically would have been called the intention to treat effect), the missing at random (MAR) assumption is questionable if patients who don’t dropout remain on their randomised treatment while those who dropout discontinue their randomised treatment (see my previous post). In particular, analyses of such data assuming MAR effectively impute the post dropout outcomes as if the patients were still on their randomised treatment.
Missing data
Understanding missing at random dropout using DAGs
I previously wrote a post about the meaning of missing at random for longitudinal data in clinical trials, stemming from an earlier question from someone. Somebody recently asked an excellent question in the comments to this post, which here I’ll follow-up on using directed acyclic graphs (DAGs). The idea of using DAGs to under missingness assumptions has been written about by a number of authors, including Daniel et al and Thoemmes and Mohan.
Critical bug fix for smcfcs in Stata
At a recent missing data course run by a colleague, users of my multiple imputation program smcfcs in Stata 15.1 found that when imputing on a simulated dataset, smcfcs took much longer to run and issued many more rejection sampling warnings than those running using Stata 14.1. Moreover, the point estimates for the substantive/analysis model obtained by those using Stata 15.1 were dramatically different to those using Stata 14.1, with the former being very biased relative to the true parameter values.