Limitations of claims and registry data in surgical oncology research.
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.
Nathan
Hari
H
Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Pawlik
Timothy M
TM
D015982:Bias; D035843:Biomedical Research; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D007344:Insurance Claim Reporting; D008495:Medical Oncology; D006278:Medicare; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000592:standards; D012042:Registries; D020379:Risk Adjustment; D018426:SEER Program / Q000592:standards; D013043:Specialties, Surgical / Q000592:standards; D014481:United States