BACKGROUND: Research in plastic surgery often includes bilateral procedures. This gives rise to issues with clustered data. Clustering is when individual data points within a data set are internally related. However, many authors do not account for clustering within their data, which can lead to incorrect statistical conclusions.
METHODS: In February of 2020, the authors searched PubMed to investigate the prevalence of reporting issues with bilateral breast procedures in plastic surgery literature. The review focused on breast surgery, as it often involves bilateral procedures and, therefore, clustering. Based on the review, the authors developed guidelines for how to identify and address clustered data. The guidelines were modified by a multidisciplinary group consisting of a biostatistician with expertise in clustered data at the Section of Biostatistics, University of Copenhagen, and three doctors (M.D.s and Ph.D.s) with expertise in statistical analysis and scientific methodology from the Copenhagen University Hospital, Rigshospitalet.
RESULTS: A total of 113 studies were included in the review. Seventy-five studies (66 percent) contained clustered data, but only eight studies (11 percent) took clustering into account in the statistical analysis. These results were used to develop the Clustered Data, or CLUDA, reporting guidelines which consist of two sections: one to identify clustering and one for reporting and analyzing clustered data.
CONCLUSIONS: Clustered data are abundant in plastic surgery literature. The authors propose using the Clustered Data reporting guidelines to identify and report clustered data and consulting with a biostatistician when designing a study.
- Research Design
- Surgery, Plastic