I was recently asked about whether smcfcs, my R and Stata packages for multiple imputation of covariates, can accommodate non-linear relationships between covariates. The answer is yes, and in this post I’ll illustrate how this can be done.
Jonathan Bartlett
Maximum likelihood multiple imputation
I just came across a very interesting draft paper on arXiv by Paul von Hippel on ‘maximum likelihood multiple imputation’. von Hippel has made many important contributions to the multiple imputation (MI) literature, including the paper which advocated that one ‘transform then impute’ when one has interaction or non-linear terms in the substantive model of interest. The present paper on maximum likelihood multiple imputation is in its seventh draft on arXiv, the first being released back in 2012. I haven’t read every detail of the paper, but it looks to me to be another thought provoking and potentially practice changing paper. This post will not attempt by any means to cover all of the important points made in the paper, but will just highlight a few.
Randomisation as the basis for inference in trials
Today I was lucky enough to listen Prof William Rosenberger present the 15th Armitage lecture in Cambridge. Prof Rosenberger has worked extensively on randomisation in trials in various respects (see his book), and he delivered a really interesting talk. The talk can now be viewed online here.