Zusammenfassung
Ziel
Bereitstellung eines GRADE-Leitfadens für die Bewertung des Bias-Risikos eines Evidenzkörpers,
wenn in systematischen Übersichtsarbeiten Daten von dichotomen und kontinuierlichen
Endpunkten fehlen.
Studiendesign und -setting
Systematische Bestandsaufnahme der publizierten methodologischen Forschung, iterative
Diskussionen, Erprobung in systematischen Übersichtsarbeiten sowie Feedback der GRADE-Arbeitsgruppe.
Ergebnisse
Der methodische Zugang beginnt mit einer primären Meta-Analyse unter Verwendung einer
Analyse der vollständigen Fälle („complete case analysis“), gefolgt von Sensitivitäts-Meta-Analysen, in denen für jede Studie mit fehlenden
Daten Imputationen vorgenommen werden und anschließend über alle Studien hinweg gepoolt
wird. Für dichotome Endpunkte empfehlen wir die Verwendung des „plausiblen Worst-Case“,
in dem die Review-Autorinnen und -autoren davon ausgehen, dass Studien mit fehlenden
Daten in den Behandlungsarmen proportional höhere Ereignisraten aufweisen als Studien
mit kompletten Daten. Für kontinuierliche Ergebnismaße werden die imputierten Mittelwerte
von anderen Studien innerhalb der systematischen Übersichtsarbeit und die Standardabweichungen
von der medianen Standardabweichung der Kontrollgruppen aller Studien herangezogen.
Schlussfolgerung
Wenn die Ergebnisse der primären Meta-Analyse gegenüber den extremsten Annahmen, die
als plausibel angesehen werden, robust sind, stuft man die Vertrauenswürdigkeit der
Evidenz aufgrund des Bias-Risikos wegen fehlender Teilnehmer-Ergebnisdaten nicht herab.
Wenn sich die Ergebnisse als nicht robust gegenüber plausiblen Annahmen erweisen,
würde man die Vertrauenswürdigkeit der Evidenz aufgrund des Bias-Risikos herabstufen.
Abstract
Objective
To provide GRADE guidance for assessing risk of bias across an entire body of evidence
consequent on missing data for systematic reviews of both binary and continuous outcomes.
Study Design and Setting
Systematic survey of published methodological research, iterative discussions, testing
in systematic reviews, and feedback from the GRADE Working Group.
Results
Approaches begin with a primary meta-analysis using a complete case analysis followed
by sensitivity meta-analyses imputing, in each study, data for those with missing
data, and then pooling across studies. For binary outcomes, we suggest use of “plausible
worst case” in which review authors assume that those with missing data in treatment
arms have proportionally higher event rates than those followed successfully. For
continuous outcomes, imputed mean values come from other studies within the systematic
review and the standard deviation (SD) from the median SDs of the control arms of
all studies.
Conclusions
If the results of the primary meta-analysis are robust to the most extreme assumptions
viewed as plausible, one does not rate down certainty in the evidence for risk of
bias due to missing participant outcome data. If the results fail to prove robust
to plausible assumptions, one would rate down certainty in the evidence for risk of
bias.
Keywords
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Article info
Publication history
Published online: February 28, 2020
Footnotes
☆Übersetzt und adaptiert von: Guyatt, G. H., Ebrahim, S., Alonso-Coello, P., Johnston, B. C., Mathioudakis, A. G., Briel, M., Mustafa, R. A., Sun, X., Walter, S. D., Heels-Ansdell, D., Neumann, I., Kahale, L. A., Iorio, A., Meerpohl, J., Schunemann, H. J., & Akl, E. A. (2017). GRADE guidelines 17: assessing the risk of bias associated with missing participant outcome data in a body of evidence. J Clin Epidemiol, 87, 14-22.
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© 2019 Published by Elsevier Inc.
