Abstract
BACKGROUND: The estimand framework was introduced into guidance on good clinical practice to address a variety of shortcomings and ambiguities in the reporting of trials, including the use of terms such as "intention to treat" and the handling of non-adherence to treatment. The framework was primarily grounded in individually randomised trials, and some thorny issues still cloud understanding of its application to cluster randomised trials. ESTIMANDS IN CLUSTER RANDOMISED TRIALS: This commentary addresses some of the challenges in thinking about the estimands behind cluster randomised trials. These challenges include informative cluster size-the possibility that a treatment effect may be modified by the size of the cluster. In particular, we consider the perspectives of different actors-a population-level decision maker or politician, a cluster manager, and a patient-and examine possible estimands for each, and how they differ. CONCLUSIONS: In the cluster randomised trial context, the estimand framework can be complex to navigate. Different perspectives lead to different estimands. We caution against abandoning careful statistical modelling. This is particularly true in the presence of informative cluster size, where modelling any interaction between cluster size and treatment effect could be useful from a number of perspectives.