Evidence map›Paper›PMID 19236120›Full record

ArticleDrug safety2009

Drug-versus-drug adverse event rate comparisons: a pilot study based on data from the US FDA Adverse Event Reporting System.

Alan M Hochberg, Ronald K Pearson, Donald J O'Hara, Stephanie J Reisinger

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Drug safety, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alan M HochbergProSanos Corporation, Harrisburg, Pennsylvania 17101, USA. alan.hochberg@prosanos.com
Ronald K Pearson
Donald J O'Hara
Stephanie J Reisinger

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA number of published studies compare adverse event rates for drugs on the basis of reports in the US FDA Adverse Event Reporting System (AERS). While the AERS data have the advantage of timely availability and a large capture population, the database is subject to many significant biases, and lacks complete patient information that would allow for correction of those biases. The accuracy of comparative AERS-based data mining has been questioned, but has not been systematically studied.

objectiveTo determine whether AERS could be used as a data source to accurately compare the adverse event rates for pairs of drugs, using pre-defined, stringent criteria to dictate whether a given pair of drugs was considered eligible for such a comparison.

methodsThe Fisher's Exact test was utilized to detect differences in adverse event rates between such pairs of drugs. Concordance was determined between statistically significant AERS-based adverse event rate differences, and adverse event rate differences published in the literature from clinical trials and case-control studies. The conditions for validity included (i) data that are free of 'extreme duplication' in AERS reports; (ii) drugs used in similar patient populations; (iii) drugs used for similar indications; (iv) drugs used with the same spectrum of concomitant medications; and (v) drugs not widely disparate in time on the market.

resultsFor 19 drugs studied, a total of 36 evaluable adverse event rate comparisons were identified. Comparisons were classified as favouring 'drug A', favouring 'drug B' or detecting no difference. Concordance for the resulting 3x3 table (AERS vs literature) gave a kappa statistic of 0.654, indicating moderately good agreement. In only two cases was there absolute discordance, with AERS designating one drug as having a lower rate, while the published study designated the other drug as having a lower rate, with respect to a given adverse event.

conclusionsThis pilot study encourages further research regarding the use of spontaneous report databases such as AERS, under stringently defined conditions, to compare adverse event rates for drugs. While not hypothesis proving, such estimates can be used for purposes such as generating hypotheses for controlled studies, and for designing those studies.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsClinical Trials as TopicDatabases, FactualDrug PrescriptionsHumansPilot ProjectsUnited StatesUnited States Food and Drug Administration

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.