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Gesundheitswesen 2021; 83(S 02): S69-S76
DOI: 10.1055/a-1633-3827
Übersichtsarbeit

Nutzung von Sekundärdaten für die pharmakoepidemiologische Forschung – machen wir das Beste draus!

Secondary Data for Pharmacoepidemiological Research – Making the Best of It!
Iris Pigeot
1   Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Abteilung Biometrie und EDV, Bremen, Deutschland
2   Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
,
Bianca Kollhorst
1   Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Abteilung Biometrie und EDV, Bremen, Deutschland
,
Vanessa Didelez
1   Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Abteilung Biometrie und EDV, Bremen, Deutschland
2   Fachbereich Mathematik und Informatik, Universität Bremen, Bremen, Deutschland
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Zusammenfassung

In Studien mit Sekundärdaten wie Abrechnungsdaten von Krankenkassen wird man häufig vor methodische Herausforderungen gestellt, die v. a. durch die Zeitabhängigkeit, aber auch durch ungemessenes Confounding entstehen. In diesem Paper stellen wir Strategien vor, um verschiedene Biasquellen zu vermeiden und um den durch ungemessenes Confounding entstehenden Bias abzuschätzen. Wir illustrieren das Prinzip der Targets Trials, marginale Strukturmodelle und instrumentelle Variablen anhand von Studien mit der GePaRD Datenbank. Abschließend werden die Chancen und Limitationen von Record Linkage diskutiert, um fehlende Information in den Daten zu ergänzen.

Abstract

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.

Schlüsselwörter

Confounding - Datenlinkage - instrumentelle Variablen - marginale Strukturmodelle - Target Trials - Verzerrung

Key words

bias - confounding - instrumental variables - marginal structure models - record linkage - target trial


Publication History

Article published online:
25 October 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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