Algorithmus zur Auswahl von Studientypen in systematischen Reviews

Zusammenfassung

Hintergrund: Der Gemeinsame Bundesausschusses schreibt systematischen Übersichtsarbeiten von randomisierten Studien die höchstmögliche Evidenzstufe Ia für die Nutzenbewertung therapeutischer Interventionen zu. Widersprüchliche Ergebnisse zwischen randomisierten und anderen Studientypen sind nicht zwingend auf das Studiendesign zurückzuführen.

Methoden: Ein Ziel der Studie war, systematische und nicht-systematische Reviews mit Aussagen zum Einschluss von Studientypen in systematischen Reviews zu identifizieren. Ein weiteres Ziel war die Entwicklung eines Algorithmus, der die Auswahl von Studientypen in systematischen Reviews erleichtern sollte.

Ergebnisse: In 85% der 42 identifizierten Artikel wurde unterstützt, dass nicht-randomisierte Studien neben randomisierten Studien in systematischen Reviews eingeschlossen werden sollten. Dies gilt insbesondere für die Evaluierung des behandlungsassoziierten Schadens. Es wurde ein Algorithmus mit den 4 Entscheidungspunkten Nachbeobachtungsdauer, Ereignishäufigkeit, Endpunkte und Studientypen entwickelt.

Schlussfolgerungen: Für die Evaluierung der Wirkung und der unerwünschten Ereignisse ist es oft erforderlich, verschiedene Studientypen einzuschließen. Der Algorithmus macht multiple Studientypen bewusst, die für den Einschluss in systematischen Reviews in Erwägung gezogen werden sollten.

Abstract

Background: The Federal Joint Committee (Gemeinsamer Bundesausschuss) assigns the highest evidence level of Ia to systematic reviews of randomized controlled trials in evaluating the benefit of therapeutic interventions. Contradictory results among randomized and non-randomized studies may not always be caused by the study design.

Methods: The objective of the study was to identify statements in systematic or non-systematic reviews about the choice of study designs in systematic reviews. Another objective was to develop an algorithm to facilitate the choice of appropriate study designs in systematic reviews.

Results: The inclusion of non-randomized in addition to randomized study designs was supported by 85% of the 42 identified articles. A strong reason was the need to evaluate the possible treatment-associated harm. The developed algorithm included the 4 decision points of length of follow-up, frequency of events, outcomes, and study designs.

Conclusions: If the benefit and the harm of a therapeutic intervention is planned to be evaluated, then often multiple study designs are required to be included. The algorithm provides guidance on which study designs should be considered for inclusion in systematic reviews.

Schlüsselwörter

Studiendesign - Algorithmus - Klinische Studien - Systematische Reviews

Key words

systematic reviews - study design - algorithm - clinical studies

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