gms | German Medical Science

15. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie

Gesellschaft für Arzneimittelforschung und Arzneimittelepidemiologie

20.11. - 21.11.2008, Bonn

Signal generation in the database of the German Net of Regional Pharmacovigilance Centers

Methoden der Signalgenerierung in der Datenbank des Netzwerks Regionaler Pharmakovigilanzzentren

Meeting Abstract

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  • corresponding author Marietta Rottenkolber - Institute for Medical Informatics, Biometry and Epidemiology, Ludwigs-Maximilians-Universität München, Munich, Germany
  • Joerg Hasford - Institute for Medical Informatics, Biometry and Epidemiology, Ludwigs-Maximilians-Universität München, Munich, Germany

Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA). 15. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie. Bonn, 20.-21.11.2008. Düsseldorf: German Medical Science GMS Publishing House; 2008. Doc08gaa13

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gaa2008/08gaa13.shtml

Veröffentlicht: 6. November 2008

© 2008 Rottenkolber 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 vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Background and aim: It has been repeatedly shown that Spontaneous Reporting Systems are the most effective monitoring source about adverse drug reactions (ADRs) after drug approval. One disadvantage of such a system is the unknown number of exposed persons. So it is impossible to estimate reliable incidence rates. For that reason signal detection measures are developed.

Material and method: ADRs causing hospitalization to departments of internal medicine are collected in the database of the German Net of Regional Pharmacovigilance Centers since 1996. This database is mined with the common measures of disproportionality, the Proportional Reporting Ratio [1], the Reporting Odds Ratio [2], and the Empirical Bayes Geometric Mean [3]. Additionally, the data are analyzed with a lasso regression model.

Results: The database of the German Net of Regional Pharmacovigilance Centers contains approximately 8,000 reports. The commonly used measures all analyse a fourfold contingency table, separately for each drug-ADR combination. The aim of these methods is to find combinations which show a great difference between observed and expected number of reports. Because these methods only focus on ‘single drugs – single event’ combinations, they completely ignore confounding, especial confounding by concomitant medication. Many combinations are just highlighted because the drug is given always with another drug that elicits the ADR. To reduce the number of false positive signals, we use a logistic regression technique to analyse the data. With lasso regression models [4] we can correct the results for confounding and analyse the influence of many drugs simultaneously.

Conclusion: The measures of disproportionality analyse only ‘single drug - single event’ combinations. Because ADRs are often induced by interactions of two or more drugs other methods like the lasso regression model are more appropriate to find such combinations.

Data Collection was supported by BfArM: Fo. V-5329/68605/2008-2010


References

1.
Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483-6.
2.
Egberts AC, Meyboom RH, van Puijenbroek EP. Use of measures of disproportionality in pharmacovigilance: three Dutch examples. Drug Saf. 2002;25(6):453-8.
3.
DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System. The American Statistician. 1999;53:177-202.
4.
Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Statist Soc B. 1996;58(1):267-88.