Evidence map›Paper›PMID 34689339›Full record

ReviewClinical pharmacology and therapeutics2022

Marketing Authorization Applications Made to the European Medicines Agency in 2018-2019: What was the Contribution of Real-World Evidence?

Robert Flynn, Kelly Plueschke, Chantal Quinten, Valerie Strassmann, Ruben G Duijnhoven, Maria Gordillo-Marañon, Marcia Rueckbeil, Catherine Cohet, Xavier Kurz

Abstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 5 pooled it
–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

60 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  17. Real-world data and evidence: pioneering frontiers in precision oncology.Journal of Zhejiang University. Science. B · 2025
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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

9 authors.

Robert FlynnData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Kelly PlueschkeData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Chantal QuintenData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Valerie StrassmannPharmacovigilance Office, Quality and Safety of Medicines, Human Medicines Division European Medicines Agency, Amsterdam, The Netherlands.
Ruben G DuijnhovenData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Maria Gordillo-MarañonData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Marcia RueckbeilData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Catherine CohetData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.
Xavier KurzData Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Information derived from routinely collected real-world data has for a long time been used to support regulatory decision making on the safety of drugs and has more recently been used to support marketing authorization submissions to regulators. There is a lack of detailed information on the use and types of this real-world evidence (RWE) as submitted to regulators. We used resources held by the European Medicines Agency (EMA) to describe the characteristics of RWE included in new marketing authorization applications (MAAs) and extensions of indication (EOIs) for already authorized products submitted to the EMA in 2018 and 2019. For MAAs, 63 of 158 products (39.9%) contained RWE with a total of 117 studies. For 31.7% of these products, the RWE submitted was derived from data collected before the planned authorization. The most common data sources were registries (60.3%) followed by hospital data (31.7%). RWE was mainly included to support safety (87.3%) and efficacy (49.2%) with cohort studies being the most frequently used study design (88.9%). For EOIs, 28 of 153 products (18.3%) contained RWE with a total of 36 studies. For 57.1% of these products, studies were conducted prior to the EOIs. RWE sources were mainly registries (35.6%) and hospital data (27.0%). RWE was typically used to support safety (82.1%) and efficacy (53.6%). Cohort studies were the most commonly used study design (87.6%). We conclude that there is widespread use of RWE to support evaluation of MAAs and EOIs submitted to the EMA and identify areas where further research is required.

Indexed as

Data CollectionDecision MakingDrug ApprovalEuropeEvidence-Based MedicineGovernment AgenciesGovernment RegulationHumans

Identifiers

PMID34689339
PMCPMC9299056

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.