Good Signal Detection Practices: Evidence from IMI PROTECT

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Abstract

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.

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Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

S J Evans, P Waller, S. Davis (2002)

The process of generating 'signals' of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical approaches to the data have been under-utilised. Using the UK Yellow Card database, we have developed and evaluated a statistical aid to signal generation called a Proportional Reporting Ratio (PRR). The proportion of all reactions to a drug which are for a particular medical condition of interest is compared to the same proportion for all drugs in the database, in a 2 x 2 table. We investigated a group of newly-marketed drugs using as minimum criteria for a signal, 3 or more cases, PRR at least 2, chi-squared of at least 4. The database was used to examine retrospectively 15 drugs newly-marketed in the UK, with the highest levels of ADR reporting. The method identified 481 signals meeting the minimum criteria during the period 1996-8. Further evaluation of these showed that 70% were known adverse reactions, 13% were events which were likely to be related to the underlying disease and 17% were signals requiring further evaluation. Proportional reporting ratios are a valuable aid to signal generation from spontaneous reporting data which are easy to calculate and interpret, and various refinements are possible.

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Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

Paul Stang, Patrick B Ryan, Judith A Racoosin … (2010)

The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.

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A Bayesian neural network method for adverse drug reaction signal generation.

Mark A. Bate, Diana M. Lindquist, I Anna S Olsson … (1998)

The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35,000 new reports are added quarterly. The task of trying to find new drug-ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug-ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril-coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin-acne; digoxin-rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug-ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.

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Author and article information

Contributors

Antoni F. Z. Wisniewski:

ORCID: http://orcid.org/0000-0002-0536-5514

antoni.wisniewski@astrazeneca.com

Journal

Journal ID (nlm-ta): Drug Saf

Journal ID (iso-abbrev): Drug Saf

Title: Drug Safety

Publisher: Springer International Publishing (Cham )

ISSN (Print): 0114-5916

Publication date (Electronic): 7 March 2016

Publication date PMC-release: 7 March 2016

Publication date (Print): 2016

Volume: 39

Pages: 469-490

Affiliations

[ ]AstraZeneca, Macclesfield, UK

[ ]Pfizer, Walton-on-the-Hill, Surrey, UK

[ ]INSERM, UMR_S1142, LIMICS, Paris, France

[ ]Department of Public Health and Medical Informatics, CHU University Hospital of Saint Etienne, Saint-Étienne, France

[ ]Novartis, Basel, Switzerland

[ ]European Medicines Agency, London, UK

[ ]Uppsala Monitoring Centre, Uppsala, Sweden

[ ]Agencia Española de Medicamentos y Productos Sanitarios, Madrid, Spain

[ ]Bayer Pharma AG, Berlin, Germany

[ ]GlaxoSmithKline, London, UK

[ ]Medicines and Healthcare Products Regulatory Agency, London, UK

[ ]Data Clarity Consulting, Stockport, UK

[ ]F. Hoffmann-La Roche, Basel, Switzerland

[ ]GlaxoSmithKline, Wavre, Belgium

[ ]Bayer Pharma AG, Wuppertal, Germany

Author information

Antoni F. Z. Wisniewski http://orcid.org/0000-0002-0536-5514

Article

Publisher ID: 405

DOI: 10.1007/s40264-016-0405-1

PMC ID: 4871909

PubMed ID: 26951233

SO-VID: 4d9fe58b-425f-4887-94be-027201be3ec6

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Funding

Funded by: Innovative Medicine Initiative Joint Undertaking

Award ID: 115004

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Good Signal Detection Practices: Evidence from IMI PROTECT

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Good practices in (cog-neuro) science and science communication

Most cited references 70

Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

A Bayesian neural network method for adverse drug reaction signal generation.

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Cited by 44

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