Evidence map›Paper›PMID 38046416›Full record

ArticleFrontiers in medicine2023

Measuring the impact of monoclonal antibody therapies.

Ka Keat Lim, Kenrick Ng, Sathiyaa Balachandran, Mark D Russell, Amy Boalch, Alasdair Sinclair, Bolaji Coker, Krishna Vinnakota, Rashid Mansoor, Abdel Douiri and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 24% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. 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

12 authors at 5 institutions in 1 country.

Ka Keat Lim *Department of Population Health Sciences, King's College London, London, United Kingdom.
Kenrick Ng *Cancer Institute, University College London, London, United Kingdom.
Sathiyaa Balachandran *Centre for Rheumatic Diseases, King's College London, London, United Kingdom.
Mark D RussellCentre for Rheumatic Diseases, King's College London, London, United Kingdom.
Amy BoalchCentre for Rheumatic Diseases, King's College London, London, United Kingdom.
Alasdair SinclairUniversity College London Hospitals NHS Foundation Trust, London, United Kingdom.
Bolaji CokerNIHR Biomedical Research Centre, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom.
Krishna VinnakotaUniversity College London Hospitals NHS Foundation Trust (UCLH), London, United Kingdom.
Rashid MansoorDepartment of Population Health Sciences, King's College London, London, United Kingdom.
Abdel DouiriDepartment of Population Health Sciences, King's College London, London, United Kingdom.
Lara V MarksWhatisBiotechnology.org, Sevenoaks, United Kingdom.
Steven SacksPeter Gorer Department of Immunology, King's College London, London, United Kingdom.
King's College London · GBUniversity College London · GBGuy's and St Thomas' NHS Foundation Trust · GBSevenoaks Hospital · GBUniversity College London Hospitals NHS Foundation Trust · GB

Funding

Medical Research Council MC_PC_20057
6 · The paper itself

Abstract

Objective: Monoclonal antibody (Mab) treatments have significantly improved the quality and quantity of life, but they are some of the most expensive treatments, resulting in a degree of hesitancy to introduce new Mab agents. A system for estimating the effect of Mab drugs, in general, would optimally inform health strategy and fully realize how a single scientific discovery can deliver health benefits. We evaluated such a method with several well-established Mab regimens. Methods: We selected five different Mab regimens in oncology and rheumatology in England. We carried out two systematic literature reviews and meta-analyses to assess health outcomes (Health Assessment Questionnaire-Disability Index for rheumatoid arthritis; overall mortality for melanoma) from real-world data and compared them to the outcomes from randomized control trials (RCTs). We applied economic modeling to estimate the net monetary benefits for health outcomes for the estimated patient population size for each Mab regimen. Results: Meta-analyses of 27 eligible real-world data (RWD) sets and 26 randomized controlled trial (RCT) sets found close agreement between the observed and expected health outcomes. A Markov model showed the net positive monetary benefit in three Mab regimens and the negative benefit in two regimens. However, because of limited access to NHS data, the economic model made several assumptions about the number of treated patients and the cost of treatment to the NHS, the accuracy of which may affect the estimation of the net monetary benefit. Conclusion: RCT results reliably inform the real-world experience of Mab treatments. Calculation of the net monetary benefit by the algorithm described provides a valuable overall measure of the health impact, subject to the accuracy of data inputs. This study provides a compelling case for building a comprehensive, systematized, and accessible database and related analytics, on all Mab treatments within health services.

Indexed as

cost benefit evaluationmeta-analysismonoclonal antibodiesreal world data (RWD)systematic literature review

Identifiers

PMID38046416
PMCPMC10690588
OpenAlexW4388705142

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.