Nathan Hari, Pawlik Timothy M
Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ann Surg Oncol. 2008 Feb;15(2):415-23. doi: 10.1245/s10434-007-9658-3. Epub 2007 Nov 7.
Studies based on large population-based data sets, such as administrative claims data and tumor registry data, have become increasingly common in surgical oncology research. These data sets can be acquired relatively easily, and they offer larger sample sizes and improved generalizability compared with institutional data. There are, however, significant limitations that must be considered in the analysis and interpretation of such data. Invalid conclusions can result when insufficient attention is paid to issues such as data quality and depth, potential sources of bias, missing data, type I error, and the assessment of statistical significance. This article reviews some important limitations of population-based data sets and the methods used to analyze them. The candid reporting of these issues in the literature and an increased awareness among surgical oncologists of these limitations will ensure that population-based studies in the surgical oncology literature achieve high standards of methodological quality and clinical utility.
基于大规模人群数据集开展的研究，如管理索赔数据和肿瘤登记数据，在外科肿瘤学研究中已变得越来越普遍。这些数据集相对容易获取，与机构数据相比，它们提供了更大的样本量并具有更高的普遍性。然而，在分析和解释此类数据时，必须考虑到一些重大限制。如果对数据质量和深度、潜在偏差来源、缺失数据、I型错误以及统计显著性评估等问题不够重视，可能会得出无效结论。本文回顾了基于人群的数据集的一些重要限制以及用于分析这些数据集的方法。在文献中坦率地报告这些问题，并提高外科肿瘤学家对这些限制的认识，将确保外科肿瘤学文献中的基于人群的研究达到方法学质量和临床实用性的高标准。
