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

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

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