Plast Reconstr Surg. 2022 Jun 10. doi: 10.1097/PRS.0000000000009293. Online ahead of print.
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 2020, we searched PubMed to investigate the prevalence of reporting issues with bilateral breast procedures in plastic surgery literature. The review focused on breast surgery, since it often involves bilateral procedures and therefore clustering. Based on the review, we 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 medical doctors and PhDs 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%) contained clustered data, but only 8 studies (11%) took clustering into account in the statistical analysis. These results were used to develop the CLUDA (CLUstered DAta) reporting guidelines which consists of two sections: one to identify clustering and one for reporting and analyzing clustered data.
CONCLUSIONS: Clustered data is abundant in plastic surgery literature, and we propose using the CLUDA reporting guidelines to identify and report clustered data and to consult a biostatistician when designing the study.