Non-trivial wedges

During February, I’ve been really bad at blogging \(-\) I’ve only posted one entry advertising our workshop at the RSS, later this month. I have spent a lot of time working in collaboration with colleagues at UCL and the London School of Hygiene and Tropical Medicine to prepare a special issue of the journal Trials.

We’ve prepared 6 articles on the Stepped Wedge (SW) design. This is a relatively new design for clinical trials \(-\) it’s basically a variant of cluster RCTs, in which all clusters start the study in the control arm and then sequentially switch to the intervention arm, in a random order, until all the clusters are given the intervention.

There are some obvious limitations to this design (first and foremost the fact that there may be a time effect over and above the intervention effect, which means that time needs to be controlled for, to avoid bias). But, as we show in our several articles, there may be some benefits in applying it \(-\) I think we’ve been very careful in detailing them, as practitioners need to be fully aware of the drawbacks.

The paper I’ve been working on mostly is about sample size calculations for a SW trial. Some authors have presented analytical formulae to do these, but while they work in specific circumstances, there are several instances in which the features of the SW formulation (time effect, repeated measurements on the same individuals in the clusters, etc) are better handled through a simulation-based approach, which is what we describe in details in our paper.

I’m also finalising a R package in which I’ll collect the functions I’ve prepared to sort-of-automate the calculations, for a set of relatively general situations. I’m planning on naming the package SWSamp (Samp have won today, so I’m all up for it, right now \(-\) we’ll see how they when I get closer to finishing it, though…).