Pigeot Iris, Kollhorst Bianca, Didelez Vanessa
Leibniz-Institut für Präventionsforschung und Epidemiologie - BIPS, Abteilung Biometrie und EDV, Bremen, Deutschland.
Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland.
Gesundheitswesen. 2021 Nov;83(S 02):S69-S76. doi: 10.1055/a-1633-3827. Epub 2021 Oct 25.
Studies using secondary data such as health care claims data are often faced with methodological challenges due to the time-dependence of key quantities or unmeasured confounding. In the present paper, we discuss approaches to avoid or suitably address various sources of potential bias. In particular, we illustrate the target trial principle, marginal structural models, and instrumental variables with examples from the "GePaRD" database. Finally, we discuss the strengths and limitations of record linkage which can sometimes be used to supply missing information.
使用诸如医疗保健理赔数据等二手数据的研究,由于关键数量的时间依赖性或未测量的混杂因素,常常面临方法学上的挑战。在本文中,我们讨论了避免或适当处理各种潜在偏差来源的方法。特别是,我们用“GePaRD”数据库中的例子说明了目标试验原则、边际结构模型和工具变量。最后,我们讨论了记录链接的优点和局限性,记录链接有时可用于提供缺失信息。