The missing at random (MAR) assumption plays an extremely important role in the context of analysing datasets subject to missing data. Its importance lies primarily in the fact that if we are willing to assume data are MAR, we can identify (estimate) target parameters. There are a variety of methods for handling data which are assumed to be MAR. One approach is estimation of a model for the variables of interest using the method of maximum likelihood. In the context of randomised trials, primary analyses are sometimes based on methods which are valid under MAR, such linear mixed models (MMRM). A key concern however is whether the MAR assumption is plausibly valid in any given situation.
Running simulations in R using Amazon Web Services
I’ve recently been working on some simulation studies in R which involve computer intensive MCMC sampling. Ordinarily I would use my institution’s computing cluster to do these, making use of the large number of computer cores, but a temporary lack of availability of this led me to investigate using Amazon’s Web Services (AWS) system instead. In this post I’ll describe the steps I went through to get my simulations going in R. As background, I am mainly a Windows user, and had never really used the Linux operating system. Nonetheless, the process wasn’t actually too tricky to get going in the end, and it’s enabled me to get the simulations completed far far more quickly than if I’d just used my desktop’s 8 cores. The advantages of using a cloud computing resource (from my perspective) is that in principle you can use as little or as much computing power as you need or want, and it is always available – you don’t have to compete against other user’s demands, as would typically be the case on an academic institution’s computer cluster.
smcfcs in R – updated version 1.1.1 with critical bug fix
For any users of my R package smcfcs, I’ve just released a new version (1.1.1), which along with a few small changes, includes a critical bug fix. The bug affected imputation of categorical (binary and categorical variables with more than two levels) when the substantive model is linear regression (other substantive model types were not affected). All users should update to the new version, which is available on CRAN.