gms | German Medical Science

54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

07. bis 10.09.2009, Essen

How frequent are meta-analyses with “double-zero” studies in systematic reviews?

Meeting Abstract

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  • Oliver Kuß - Institut für Medizinische Epidemiologie, Biometrie und Informatik, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale)
  • Michaela Wandrey - Klinik für Urologie, Kinderurologie und Onkologische Eingriffe, Diakonissenkrankenhaus Dessau gGmbH, Dessau
  • Mareike Kunze - Institut für Medizinische Epidemiologie, Biometrie und Informatik, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Essen, 07.-10.09.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. Doc09gmds155

DOI: 10.3205/09gmds155, URN: urn:nbn:de:0183-09gmds1554

Veröffentlicht: 2. September 2009

© 2009 Kuß et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Introduction: Meta-analyses for rare binary (e.g., adverse events or safety) outcomes often include studies that have no events in one (“single-zero”) or even both (“double-zero”) treatment arms. A variety of methods to deal with this problem statistically have been proposed. Most of them rely on adding pseudo-observations to the respective studies, or, in case of “double-zero” studies, simple deleting them. However, simply deleting “double-zero” studies might result in biased treatment effects as those studies (with having equal numbers of outcomes in both arms) point to a null effect. We recently gave an example of a meta-analysis where ignoring “double-zero” studies missed a clearly significant and clinically relevant advantage of the off-pump technique on post-operative stroke prevalence in bypass surgery [1]. While it has been reported that about 30 % of all meta-analyses contain at least one “single-zero” study [2], there is no evidence on the frequency of meta-analyses with “double-zero” studies.

Methods: We performed a systematic review on the sample of Cochrane reviews (n = 500) from the Vandermeer [2] study. Two independent reviewers searched the full text of papers for the number of meta-analyses containing at least one “double-zero” study. Disagreement was resolved by repeating the search in the respective review and finding consensus.

Results: In the 500 Cochrane reviews we found 1394 meta-analyses with at least one “double-zero” study. These 1394 meta-analyses originate from 172 of the 500 (= 34.4 %) reviews. The median number of meta-analyses with at least one “double-zero” study in those 172 reviews was 3.5 (Min: 1, Q1: 1, Q3: 9, Max: 102).

Discussion: Meta-analyses with “double-zero” studies occur frequently in Cochrane reviews. In future work we will check if statistical methods that adequately account for those studies (refraining from 1. adding pseudo-observations or 2. simply deleting them) would eventually give clinically different results.


References

1.
Kuss O, Börgermann J. Meta-analyses with rare outcomes should use adequate methods – A case study from cardiac surgery. Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 53. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds). Stuttgart, 15.-19.09.2008. Düsseldorf: German Medical Science GMS Publishing House; 2008. Doc MBIO1-2. http://www.egms.de/en/meetings/gmds2008/08gmds049.shtml Externer Link
2.
Vandermeer B, Bialy L, Hooton N, Hartling L, Klassen TP, Johnston B, Wiebe N. Meta-analyses of safety data: a comparison of exact versus asymptotic methods. Stat Methods Med Res. 2008 Jun 18. [Epub ahead of print]