Capitalising on covariates in cluster-randomised experiments

design features
cluster-randomised experiments

Jan Vanhove


September 2, 2020

In cluster-randomised experiments, participants are assigned to the conditions randomly but not on an individual basis. Instead, entire batches (‘clusters’) of participants are assigned in such a way that each participant in the same cluster is assigned to the same condition. A typical example would be an educational experiment in which all pupils in the same class get assigned to the same experimental condition. Crucially, the analysis should take into account the fact that the random assignment took place at the cluster level rather than at the individual level.

Also typically in educational experiments, researchers have some information about the participants’ performance before the intervention took place. This information can come in the form of a covariate, for instance the participants’ performance on a pretest or some self-assessment of their skills. Even in experiments that use random assignment, including such covariates in the analysis is useful as they help to reduce the error variance. Lots of different methods for including covariates in the analysis of cluster-randomised experiments are discussed in the literature, but I couldn’t find any discussion about the merits and drawbacks of these different methods.

In order to provide such discussion, I ran a series of simulations to compare 25 (!) different ways of including a covariate in the analysis of a cluster-randomised experiment in terms of their Type-I error and their power. The article outlining these simulations and the findings is available from PsyArXiv; the R code used for the simulations as well as its output is available from the Open Science Framework. In the remainder of this post, I’ll discuss how these simulations may be useful to you if you’re planning to run a cluster-randomised experiment.

So what’s the upshot?

Please read pages 1–3 and pages 40–42 of the article :)

I know of some analytical method that you didn’t consider.

Ah, interesting! It took a long time to run these simulations (about 36 hours), during which I couldn’t use my computer for anything else, so I’m not exactly gung-ho about rerunning them just to include one additional analytic method.

But here’s what you can do. Go to the OSF page and download the files functions/generate_clustered_data.R and scripts/additional_simulations.Rmd. The latter file contains some smaller-scale simulations that don’t take as long to run. Adapt the simulations there and check if the analytical method you know of has an acceptable Type-I error rate for a variety of parameter settings. (Two examples are available, but if you can’t make sense of them, let me know.) If its Type-I error rate is acceptable, run another batch of simulations to assess its power and compare it to the power for the best-performing methods in my simulation.

If your method compares well to these best-performing methods in terms of both its Type-I error rate and its power, drop me a line, and perhaps I’ll get round to rerunning the large-scale simulations. Better still, download the other functions and scripts, include your method in functions/analyse_clustered_data.R, and adjust the other files accordingly. Then run the simulation yourself :)

I don’t think your parameter choices are relevant for my study.

Perhaps your study will feature clusters that vary more strongly in size than was the case in my simulations. Or perhaps you suspect that the intracluster correlation will be quite different from the ones that I considered. Or perhaps etc., etc., etc. It’d be better if the results of the simulations were more directly relevant to what you suspect your study will look like.

But here’s the beauty. You can go to the OSF page, download all the functions and scripts, and tailor the simulation parameters to your liking. In script/simulation_type_I_error.R and script/simulation_power.R, you can change the cluster sizes, the number of clusters, the strength of the covariate, the ICC, the effect, and the randomisation scheme. Then run these scripts and figure out which analysis method will likely retain its nominal Type-I error while maximising power.

When generating the data, you’re making some assumptions I’m not willing to make.

The assumptions are outlined in the article on pp. 23–24, and they are made more explicit in the function that generates the data (functions/generate_clustered_data.R). Perhaps they’re unrealistic. For instance, the data are all drawn from normal distributions, the covariate is linearly related to the outcome, etc. If you want to revise these assumptions, you’ll have to edit this function. (Test the function extensively afterwards!) Then re-run the simulations, with the simulation parameters tailored to your study.

Which journal will this article be published in?

At the moment, I don’t intend to submit this article to any journal. The main reason is that anyone who may be interested in it already has free access to it. If anyone has any feedback, I’d be happy to hear it, but I don’t currently feel like jumping through a series of hoops in some drawn-out reviewing process.