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Record W2142581342 · doi:10.1002/cncr.29624

Reply to price and value in cancer care

2015· letter· en· W2142581342 on OpenAlexaboutno aff
Jagpreet Chhatwal, Michael S. Mathisen, Hagop M. Kantarjian

Bibliographic record

VenueCancer · 2015
Typeletter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
FundersU.S. National Library of MedicineAgency for Healthcare Research and QualityCenters for Disease Control and PreventionNational Science Foundation
KeywordsMedicineValue (mathematics)Cancer drugsGovernment (linguistics)Drug pricesActuarial scienceCancerDrugHealth careQuality (philosophy)Family medicinePublic economicsEconomicsEconomic growthPharmacologyStatisticsInternal medicine

Abstract

fetched live from OpenAlex

We appreciate the acknowledgment by Saret and colleagues of the shortcomings of their previous analysis published in Blood1 and, in particular, the different results when we used current drug prices (rather than older drug prices) to measure the cost-effectiveness of drugs used in hematologic malignancies.2 The purpose of our article was to bring attention to the rising cost of cancer drugs and the need to re-assess the value of treatment if drug prices increase. By re-analyzing the results, we found that the incremental cost-effectiveness ratios (ICERs) of the majority of the studies had increased substantially. We would like to discuss a few additional points highlighted in Saret et al's letter. First, several of our previous editorials not only discussed high drug prices but also introduced the concept of treatment value and proposed measures.3, 4 This led the American Society of Cancer Oncology and other entities to adopt the concept and expand the dialogue on treatment value in cancer.5 Second, we are extremely concerned with the use of $100,000 and $150,000/quality-adjusted life-year [QALY] as new thresholds. The $50,000 threshold is based on historic convenience and practices and is similar to the values used by the United Kingdom, Australia, Canada, and other government entities in discussions with drug companies and in new drug approvals. Setting another (and higher) threshold without thorough research would not be a step forward. In fact, a recently published report by a group of well-known health economists found that the willingness-to-pay threshold in the United States should be between $24,000 and $40,000,6 even lower than the $50,000 threshold broadly used. We think that an authoritative conclusion about the threshold could best be made by a task force or committee representing multiple stakeholders. Third, although we agree with Saret et al that there is a need to consider patent expiration in the cost-effectiveness analysis, if we used the same logic, all cost-effectiveness analyses, including the ones analyzed in our study, should include the increases in drug prices anticipated in the future (beyond 2015). The year of patent expiration is easy to assess, but it is difficult to estimate future increases in drug prices. It is worth noting that in the 5 chronic myeloid leukemia studies, the ICER values for the tyrosine kinase inhibitors versus hydroxyurea or interferon ranged from $210,000 to $426,000/QALY in 2014, whereas the majority of the reported ICERs of these studies are more than a decade old and less than $50,000/QALY. Therefore, simply referencing a study-reported ICER from past years’ drug prices would provide a misleading conclusion in light of today's drug prices. Finally, we again emphasize that neither our analysis nor that of Saret et al included any new drugs or studies after 2012. Several recent studies have shown that the ICERs of these more recent drugs/studies exceed the threshold of $50,000/QALY.7-10 Note that drug patent expiration would not have a substantial impact, if any, on the cost-effectiveness of new drugs. Also, an analysis by Howard et al11 showed that, after adjustments for inflation, the cost of cancer drugs for each additional year lived increased from $54,000 in 1995 to $207,000 in 2013. Therefore, the worsening trends of high prices for hematologic cancer drugs are of significant concern to patients and our health care system. No specific funding was disclosed. Jagpreet Chhatwal has received consulting fees from Gilead Sciences, Merck, and Complete HEOR Solutions outside the submitted work. Hagop Kantarjian is a Scholar in Health Policy at the Baker Institute and is a member of the board of directors of the American Society of Cancer Oncology. Research grants were provided by Novartis, Bristol-Myers Squibb, ARIAD, and Pfizer. Jagpreet Chhatwal, PhD Department of Health Services Research The University of Texas MD Anderson Cancer Center Houston, Texas Michael S. Mathisen, PhD Epocrates Medical Information AthenaHealth San Francisco, California Departments of Pharmacy and Leukemia The University of Texas MD Anderson Cancer Center Houston, Texas Hagop M. Kantarjian, MD Department of Leukemia The University of Texas MD Anderson Cancer Center Houston, Texas

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)0.012
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.040
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.106
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0040.008
Open science0.0030.002
Research integrity0.0400.055
Insufficient payload (model declined to judge)0.0060.003

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 head0.046
GPT teacher head0.265
Teacher spread0.219 · 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 designNot applicable
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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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