Clustering in surgical trials--database of intracluster correlations.

backgroundRandomised trials evaluation of surgical interventions are often designed and analysed as if the outcome of individual patients is independent of the surgeon providing the intervention. There is reason to expect outcomes for patients treated by the same surgeon tend to be more similar than those under the care of another surgeon due to previous experience, individual practice, training, and infrastructure. Such a phenomenon is referred to as the clustering effect and potentially impacts on the design and analysis adopted and thereby the required sample size. The aim of this work was to inform trial design by quantifying clustering effects (at both centre and surgeon level) for various outcomes using a database of surgical trials.

methodsIntracluster correlation coefficients (ICCs) were calculated for outcomes from a set of 10 multicentre surgical trials for a range of outcomes and different time points for clustering at both the centre and surgeon level.

resultsICCs were calculated for 198 outcomes across the 10 trials at both centre and surgeon cluster levels. The number of cases varied from 138 to 1370 across the trials. The median (range) average cluster size was 32 (9 to 51) and 6 (3 to 30) for centre and surgeon levels respectively. ICC estimates varied substantially between outcome type though uncertainty around individual ICC estimates was substantial, which was reflected in generally wide confidence intervals.

conclusionsThis database of surgical trials provides trialists with valuable information on how to design surgical trials. Our data suggests clustering of outcome is more of an issue than has been previously acknowledged. We anticipate that over time the addition of ICCs from further surgical trial datasets to our database will further inform the design of surgical trials.