Evidence map›Paper›PMID 35749431›Full record

ArticlePloS one2022

A framework for assessing the impact of accelerated approval.

A Lawrence Gould, Robert K Campbell, John W Loewy, Robert A Beckman, Jyotirmoy Dey, Anja Schiel, Carl-Fredrik Burman, Joey Zhou, Zoran Antonijevic, Eva R Miller and 1 more

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
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

11 authors.

A Lawrence GouldMethodology Research, BARDS, Merck & Co., Inc., Kenilworth, New Jersey, United States of America.ORCID 0000-0002-9661-4474
Robert K CampbellMolecular Pharmacology, Physiology and Biotechnology, Brown University, Providence, Rhode Island, United States of America.
John W LoewyDataForethought, Winchester, Massachusetts, United States of America.
Robert A BeckmanLombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, United States of America.
Jyotirmoy DeyData and Statistical Sciences, AbbVie, North Chicago, Illinois, United States of America.
Anja SchielDepartment for Pharmacoeconomics, Norwegian Medicines Agency, Oslo, Norway.
Carl-Fredrik BurmanData Science & AI, AstraZeneca R&D, Gothenburg, Sweden.
Joey ZhouXcovery Pharmaceuticals, Palm Beach Gardens, Florida, United States of America.
Zoran AntonijevicAbond CRO, Allendale, Michigan, United States of America.
Eva R MillerIndependent Biostatistical Consultant, Middletown Twp, Pennsylvania, United States of America.
Rui TangMethodology and Data Visualization, Biostatistics Department, Servier Pharmaceuticals US, Boston, Massachusetts, United States of America.

Funding

Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized InterventionUL1TR001409 · NCATS · GEORGETOWN UNIVERSITY · PI GONDRE-LEWIS, MARJORIE C, MELLMAN, THOMAS A · 2015 to 2024
$37.8M
NCATS NIH HHS UL1 TR001409
6 · The paper itself

Abstract

The FDA's Accelerated Approval program (AA) is a regulatory program to expedite availability of products to treat serious or life-threatening illnesses that lack effective treatment alternatives. Ideally, all of the many stakeholders such as patients, physicians, regulators, and health technology assessment [HTA] agencies that are affected by AA should benefit from it. In practice, however, there is intense debate over whether evidence supporting AA is sufficient to meet the needs of the stakeholders who collectively bring an approved product into routine clinical care. As AAs have become more common, it becomes essential to be able to determine their impact objectively and reproducibly in a way that provides for consistent evaluation of therapeutic decision alternatives. We describe the basic features of an approach for evaluating AA impact that accommodates stakeholder-specific views about potential benefits, risks, and costs. The approach is based on a formal decision-analytic framework combining predictive distributions for therapeutic outcomes (efficacy and safety) based on statistical models that incorporate findings from AA trials with stakeholder assessments of various actions that might be taken. The framework described here provides a starting point for communicating the value of a treatment granted AA in the context of what is important to various stakeholders.

Indexed as

Drug ApprovalTechnology Assessment, BiomedicalHumansTreatment OutcomeUnited StatesUnited States Food and Drug Administration

Identifiers

PMID35749431
PMCPMC9231718

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.