title: Analysis of large databases in vascular surgery.
abstract: Large databases can be a rich source of clinical and administrative information on broad populations. These datasets are characterized by demographic and clinical data for over 1000 patients from multiple institutions. Since they are often collected and funded for other purposes, their use for secondary analysis increases their utility at relatively low costs. Advantages of large databases as a source include the very large numbers of available patients and their related medical information. Disadvantages include lack of detailed clinical information and absence of causal descriptions. Researchers working with large databases should also be mindful of data structure design and inherent limitations to large databases, such as treatment bias and systemic sampling errors. Withstanding these limitations, several important studies have been published in vascular care using large databases. They represent timely, "real-world" analyses of questions that may be too difficult or costly to address using prospective randomized methods. Large databases will be an increasingly important analytical resource as we focus on improving national health care efficacy in the setting of limited resources.
authors.affiliation: Department of Vascular Surgery, Brigham and Women's Hospital, Boston, Mass 02115, USA. llnguyen@partners.org
authors.forename: Louis L
authors.initials: LL
authors.lastname: Nguyen
authors.forename: Neal R
authors.initials: NR
authors.lastname: Barshes
mesh_terms: D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D019992:Databases as Topic / Q000706:statistics & numerical data*; D019317:Evidence-Based Medicine; D006801:Humans; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design; D016896:Treatment Outcome; D014656:Vascular Surgical Procedures / Q000706:statistics & numerical data*