Evidence map›Paper›PMID 34007563›Full record

ArticleInnovations in pharmacy2019

The Imaginary Worlds of Cure Proportion Modeling: Survivorship and Reference Case Pricing of Transformative Therapies.

Paul C Langley

Abstract read
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Paul C LangleyAdjunct Professor, College of Pharmacy, University of Minnesota.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On August 6, 2019 the Institute for Clinical and Economic Review (ICER) released a set of proposed adaptations to its value assessment framework methods where the intervention under review was considered a 'single or short-term transformativetherapy' (SST). These adaptations are intended to 'complement and build upon the upcoming update to the overall ICER assessment framework…'. The purpose of this commentary is to review the proposed cure proportion modeling reference case framework for assessing the value of SSTs together with ICER's 'recommendations for fair value-based pricing …'. Following previous commentaries on the ICER value assessment framework, the question raised is whether the proposed cure proportion modeling standards meet those of normal science: is the modeling proposed capable of generating value claims for the intervention that are credible, evaluable and replicable? The proposed standards for transformative therapies do not change the underlying commitment to reference case modeling. At the same time, the cure proportion modeling proposed adaptations have to be seen in the context of the concerns expressed by ICER that their reference case model can be used to justify substantial one-off SST pricing. This follows from the ICER incremental cost per QALY willingness to pay thresholds where the SST QALY gains are sufficient, at even a $50,000 QALY cut-off, to support SST pricing in the millions of dollars. ICER has two options: (i) abandon the imaginary reference case methodology, which is the ICER core business model and would represent an ironic reversal, or (ii) attempt to bolt-on adaptations, possibly incorporating revised survivorship profiles using cure proportion modeling, that supports a modified imaginary reference case 'rescue' model for SSTs designed specifically to generate pricing recommendations that may be considered affordable.

Indexed as

competing imaginary worldscure proportion modelsrescuing reference casesurvivorshipTransformative therapies (SSTs)

Identifiers

PMID34007563
PMCPMC8127087

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.