Evidence map›Paper›PMID 28280890›Full record

ArticleEuropean journal of clinical pharmacology2017

Utilization of chi-square statistics for screening adverse drug-drug interactions in spontaneous reporting systems.

Masahiko Gosho, Kazushi Maruo, Keisuke Tada, Akihiro Hirakawa

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European journal of clinical pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  12. Detecting Drug-Drug Interactions in COVID-19 Patients.Studies in health technology and informatics · 2022
    Article
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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.

Masahiko GoshoDepartment of Clinical Trial and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8575, Japan. mgosho@md.tsukuba.ac.jp.ORCID http://orcid.org/0000-0002-5973-9163
Kazushi MaruoDepartment of Clinical Epidemiology, Translational Medical Center, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8551, Japan.
Keisuke TadaBiostatistics & Programming, Sanofi K. K, Tokyo Opera City Tower, 3-20-2, Nishi Shinjuku, Tokyo, 163-1488, Japan.
Akihiro HirakawaCenter for Advanced Medicine and Clinical Research, Nagoya University Hospital, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8560, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe proposed a statistical criterion to detect drug-drug interactions causing adverse drug reactions in spontaneous reporting systems.

methodsThe used criterion quantitatively measures the discrepancy between the observed and expected number of adverse events via chi-square statistics. We compared the performance of our method with that of Norén et al. (Stat Med 2008; 27 (16): 3057-3070) through a simulation study.

resultsWhen the number of events for a combination of two drugs was equal to or lower than two, the false positive rate for our method ranged from 0.01 to 0.08, whereas the rate for Norén's method ranged from 0.01 to 0.06. The sensitivity for our method ranged from 0.09 to 0.29, whereas the sensitivity for Norén's method ranged from 0.03 to 0.24. The area-under-the-receiver operating characteristic curve for our method was significantly larger than that for Norén's methods regardless of simulation settings. The proposed method was also applied to the Food and Drug Administration Adverse Event Reporting System database, and a recognized drug-drug interaction was detected.

conclusionsThe proposed criterion controlled false positives at an acceptable level and had higher sensitivity than that of Norén's method had when events were rare.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug InteractionsChi-Square DistributionComputer SimulationDatabases, FactualDrug-Related Side Effects and Adverse ReactionsFalse Positive ReactionsHumansROC CurveSensitivity and SpecificityUnited StatesUnited States Food and Drug AdministrationAdverse event reporting systemFalse positiveSensitivitySignal detection

Identifiers

What OpenQuestion holds

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Registered trials

None linked

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.