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.
Jonathan Bartlett
Comment on ‘Conditional estimation and inference to address observed covariate imbalance in randomized clinical trials’
Thanks to Tim Morris for letting me know about a paper just published in the journal Clinical Trials by Zhang et al, titled ‘Conditional estimation and inference to address observed covariate imbalance in randomized clinical trials’. Zhang et al propose so called conditional estimation and inference to address observed covariate imbalance in randomised trials. They introduce the setup of randomised trials with covariates 
, randomised treatment 
, and outcome 
. They begin with a framework that treats all three as random in repeated sampling, and review the unadjusted estimator of the marginal mean difference in outcome, and a covariate adjusted estimator based on earlier work by Tsiatis and others.
Combining bootstrapping and multiple imputation under uncongeniality
Tomorrow I’m giving a talk (slides here) at the Joint Statistical Meeting in Vancouver on some work I’ve been doing on combining bootstrapping with multiple imputation (MI), something I’ve written about here before. That post looked at a recent paper by Schomaker and Heumann (2018) on various ways of combining bootstrapping and MI. A more recent post discussed an arXiv paper by von Hippel (2018) on maximum likelihood multiple imputation, which also contains a nice proposal for combining bootstrap and MI. My talk this week is about how these perform when the imputation and analysis models are not congenial.