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Use of Efficiency Frontiers to Align Prices and Clinical Benefits of Biologic Therapies for Plaque Psoriasis

2024· letter· en· W4391994515 on OpenAlexaboutno aff
Alexander C. Egilman, Aaron S. Kesselheim, Jerry Avorn, Adam Raymakers, Benjamin N. Rome

Bibliographic record

VenueJAMA Dermatology · 2024
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEtanerceptPsoriasisInfliximabPlaque psoriasisPsoriasis Area and Severity IndexBiosimilarUstekinumabInternal medicineDermatology

Abstract

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Importance: The US lacks a systematic approach for aligning drug prices with clinical benefit, and traditional cost-effectiveness analysis (CEA) faces political obstacles. The efficiency frontier (EF) method offers policymakers an alternative approach. Objective: To assess how the EF approach could align prices and clinical benefits of biologic medications for plaque psoriasis and estimate price reductions in the US vs 4 peer countries: Australia, Canada, France, and Germany. Design and Setting: This health economic evaluation used the EF approach to compare the prices and clinical benefits of 11 biologics and 2 biosimilars for plaque psoriasis in the US, Australia, Canada, France, and Germany. Data were collected from February to March 2023 and analyzed from March to June 2023. Main Outcome Measures: EFs were constructed based on each biologic's efficacy, measured using the Psoriasis Area and Severity Index (PASI) 90 response rate, and annual treatment cost as of January 2023; US costs were net of estimated manufacturer rebates. Prices based on the EF were compared with traditional CEA-based prices calculated by the Institute for Clinical and Economic Review at a threshold of $150 000 per quality-adjusted life-year gained. Results: Among 13 biologics, PASI 90 response rates ranged from 17.9% (etanercept) to 71.6% (risankizumab); US net annual treatment costs ranged from $1664 (infliximab-dyyb) to $79 277 (risankizumab). The median (IQR) net annual treatment cost was higher in the US ($34 965 [$20 493-$48 942]) than prerebate costs in Australia ($9179 [$6691-$12 688]), Canada ($15 556 [$13 017-$16 112]), France ($9478 [$6637-$11 678]), and Germany ($13 829 [$13 231-$15 837]). The US EF included infliximab-dyyb (PASI 90: 57.4%; annual cost: $1664), ixekizumab (PASI 90: 70.8%; annual cost: $33 004), and risankizumab (PASI 90: 71.6%; annual cost: $79 277). US prices for psoriasis biologics would need to be reduced by a median (IQR) of 71% (31%-95%) to align with those estimated using the EF; the same approach would yield smaller price reductions in Canada (41% [6%-57%]), Australia (36% [0%-65%]), France (19% [0%-67%]), and Germany (11% [8%-26%]). Except for risankizumab, the EF-based prices were lower than the prices based on traditional CEA. Conclusions and Relevance: This economic evaluation showed that for plaque psoriasis biologics, using an EF approach to negotiate prices could lead to substantial price reductions and better align prices with clinical benefits. US policymakers might consider using EFs to achieve prices commensurate with comparative clinical benefits, particularly for drug classes with multiple therapeutic alternatives for which differences can be adequately summarized by a single outcome measurement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.137
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.137
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.005
Science and technology studies0.0000.002
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.358
GPT teacher head0.426
Teacher spread0.067 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2024
Admission routes1
Has abstractyes

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