In my work and indeed blog posts on this site I often perform simulation studies. They can be invaluable in various ways for exploring and testing the performance of statistical methods under different conditions. Recently Tim Morris, Ian White and Michael Crowther published an excellent paper in Statistics in Medicine, freely available here, on how to plan and run simulation studies. The paper contains a wealth of useful guidance and advice on how to run simulation studies, and in particular highlights some things that can cause things to go wrong with inappropriate setting of random number seeds!
Tim has an accompanying Github repository with Stata code for their illustrative example from the paper, where they simulate survival data and analyse it using a number of different survival regression models. As part of the new MSc in Data Science & Statistics here at the University of Bath, I’ve put together a short introductory tutorial on performing simulation studies using R. It can be accessed here. I hope it gives a good introduction to the key elements of programming up a simulation study in R. If anyone has comments on it or thinks I’ve omitted something important that should be covered, please get in touch via email or a comment on this page.
, 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.