ArticleInnovations in pharmacy2019
Another Rush to Judgment: The Imaginary Worlds of ICER and Recommendations in Duchenne Muscular Dystrophy.
Article in Innovations in pharmacy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Nonsense on Stilts - Part 1: The ICER 2020-2023 Value Assessment Framework for Constructing Imaginary Worlds.Innovations in pharmacy · 2020Article
- The Imaginary Worlds of Cure Proportion Modeling: Survivorship and Reference Case Pricing of Transformative Therapies.Innovations in pharmacy · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous commentaries in the Formulary Evaluation section of INNOVATIONS in Pharmacy have pointed to the lack of credibility in modeled claims for cost-effectiveness and associated recommendations for pricing by the Institute for Clinical and Economic Review (ICER). The principal objection to ICER reports has been that their modeled claims fail the standards of normal science: they are best seen as pseudoscience. The purpose of this latest commentary is to consider the recently released ICER report for Duchennemuscular dystrophy (DMD). As ICER has continued in the case of DMD to apply its modeled cost utility framework with consequent recommendations for pricing adjustments, these recommendations also lack credibility. This commentary emphasizes again not only why the ICER methodology fails to meet the standards of normal science but to point to the importance in rare diseases for accelerated approval, while recognizing that evidence generation will continue.While this assessment of the ICER DMD model does not imply any support for this methodology, a key point is the application of quality of life measures which fail to capture the experience of patients with DMD and, importantly, the interests of both patients and caregivers. While ICER would argue that even with a limited evidence base it is important to address issues of pricing and access for new products, their reports are used as justification for coverage and reimbursement by insurers and health system decision makers without recognition of their lack of scientific merit. This rush to judgement by ICER must raise concerns about potentially adverse formulary decisions that result in access restrictions on new products. If ICER is to make a contribution to the entry of new products in the health market place then it should consider an alternative methodology that generates claims that are empirically evaluable in a timeframe relevant to health decision makers. As it stands, ICER's recommendation should be rejected. This is not a research program that meets accepted scientific standards but one that relies on the willingness of an audience to accept the proposition that evidence is constructed not discovered.
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What OpenQuestion holds
Registered trials
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.