Use of estimands in cluster randomised trials: a review
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
An estimand is a clear description of the treatment effect a study aims to quantify. The ICH E9(R1) addendum lists five attributes that should be described as part of the estimand definition. However, the addendum was primarily developed for individually randomised trials. Cluster randomised trials (CRTs), in which groups of individuals are randomised, have additional considerations for defining estimands (e.g., how individuals and clusters are weighted, how cluster-level intercurrent events are handled, etc). However, it is currently unknown if estimands are being used in CRTs, or whether the considerations specific to CRTs are being described.
Methods
We reviewed 73 CRTs published between Oct 2023 and Jan 2024 that were indexed in MEDLINE. For each trial, we assessed whether the estimand for the primary outcome was described, or if not, whether it could be inferred from the statistical methods. We also assessed whether considerations specific to CRTs were described or inferable, how trials were analysed, and whether key assumptions being made in the analysis (e.g. “no informative cluster size”) could be identified.
Results
No trials attempted to describe the estimand for their primary outcome. We were able to infer the full estimand based on the five attributes outlined in ICH E9(R1) in only 49% of trials, and when including additional considerations specific to CRTs, this figure dropped to 21%. Key drivers of this ambiguity were lack of clarity around whether individual- or cluster-average effects were of interest (unclear in 63% of trials), and how cluster-level intercurrent events were handled (unclear in 21% of trials for which this was applicable). Over half of trials used mixed-effects models or GEEs with an exchangeable correlation structure, which make the assumption that there is no informative cluster size; however, only one of these trials performed sensitivity analyses to evaluate robustness of results to deviations from this assumption. There were 14% of trials that used independence estimating equations or the analysis of cluster level summaries; however, because no trials stated whether they were targeting the individual- or cluster-average effect, it was impossible to determine whether these methods implemented the appropriate weighting scheme and were thus unbiased.
Conclusion
The uptake of estimands in published cluster randomised trial articles is low, making it difficult to ascertain which questions were being investigated or whether statistical estimators were appropriate for those questions. This highlights an urgent need to develop guidelines on defining estimands that cover unique aspects of CRTs to ensure clarity of research questions in these trials.
