title: Pulmonary metastasectomy: a call for better data collection, presentation and analysis.
abstract: A systematic review of the literature for outcomes in pulmonary metastasectomy has revealed the variability in reporting and the paucity of data that would allow a clear understanding of the effectiveness of this operation. The authors, a surgeon and a mathematician, start from the experience of assessing the evidence on which the practice of pulmonary metastasectomy is based and give some simple examples on a more adequate approach to the collection and analysis of surgical data and the importance of its correct interpretation. Retrospective data analysis is constrained by the availability of data. While this can give insight on certain aspects, it is important to discern what data are necessary to give a complete understanding of the effectiveness of a practice. Typically well designed prospective studies and randomised controlled trials with a pre-specified data collection plan give more complete, consistent and reliable data than follow-up or retrospective studies. Pulmonary metastasectomy lends itself well as an example of practice based on uncertain evidence and biased reporting. All the available published studies are follow-up studies, there is no randomised controlled trial, so no control data to estimate its treatment effect on patient's survival. The pool of colorectal or sarcoma patients from which patients are selected to have a pulmonary metastasectomy is never reported on, thus it is hard to estimate the degree of selection and the influence of the surgeon's decision.
authors: Department of Cardiothoracic Surgery, NHLI, Imperial College London, London, UK.
authors: Francesca
authors: F
authors: Fiorentino
authors: Tom
authors: T
authors: Treasure
mesh_terms: D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D008175:Lung Neoplasms / Q000556:secondary* / Q000601:surgery*; D059146:Metastasectomy / Q000706:statistics & numerical data*; D012042:Registries / Q000706:statistics & numerical data
keywords: data; statistics; surgery