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Reassessing the net health benefit (NHB) of FDA approved cancer drugs with evolution of evidence using the American Society of Clinical Oncology Value Framework (ASCO-VF).

2020· article· en· W3031543727 on OpenAlexaff
Seanthel Delos Santos, Noah Witzke, Vanessa Sarah Arciero, Amanda Putri Rahmadian, Louis Everest, Kelvin Chan

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsMedicineClinical endpointClinical OncologyClinical trialInternal medicineOncologyDrug approvalFamily medicineCancerDrugPharmacology

Abstract

fetched live from OpenAlex

7011 Background: Regulatory approval of oncology drugs are often based on data presented in the primary publication of clinical trials (CT). However, clinically relevant data, such as long-term overall survival (OS) and quality of life (QOL), are often reported in subsequent publications. Therefore, this study aimed to evaluate the ASCO-VF NHB at the time of drug approval and over time as further evidence is published. Methods: All FDA approved oncology drug indications from 01/06-12/16 were reviewed to identify CTs that were scorable using the ASCO-VF version 2. Subsequent publications of included CTs relevant for scoring were identified from Web of Science with a follow-up time of 3 years from approval. Using ASCO-defined threshold scores of ≤40 for low benefit and ≥45 for substantial benefit, changes in classification of benefit were assessed at 3-years post-FDA approval. Results: We identified 57 FDA approved indications (40.4% OS, 59.6% progression-free survival (PFS) as primary endpoints) with scorable ASCO-VF CTs. Among those 57 indications, 36.8% at the time of FDA approval demonstrated substantial benefit, 10.5% demonstrated intermediate benefit, and 52.6% demonstrated low benefit. We then identified 96 subsequent publications relevant to scoring within 3-years of FDA approval, consisting of primary endpoint updates (29.2%; 14.6% OS, 12.5% PFS), secondary endpoint updates (44.8%; 16.7% OS, 7.3% PFS), new reporting of secondary endpoint (4.2% OS), safety updates (28.1%), and QOL reporting (43.8%). Upon reassessment of the NHB in subsequent publications, there was an overall change from initial classification of benefit in 36.8% of trials (17.5% became substantial, 8.8% became low, and 10.5% became intermediate). Changes in scores were mainly the result of an updated hazard ratio (35.1%), change in scoring endpoints from PFS to OS as per ASCO-VF endpoint hierarchy (8.8%), toxicity updates (57.9%), new tail of the curve bonus (12.3%), palliation bonus (14.0%), or QOL bonus (22.8%). Overall, at reassessment at 3 years post-FDA approval, 42.1% were substantial, 10.5% were intermediate, and 47.3% were low benefit. Conclusions: Only a modest proportion of FDA approved drugs have demonstrated substantial NHB at time of approval. As further evidence was published, a substantial proportion of indications have a change in classification of NHB, resulting in a small increase in the overall proportion of indications being deemed to have substantial benefit at 3 years post-approval.

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)0.082
metaresearch head score (Gemma)0.226
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.226
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0230.014
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.814
GPT teacher head0.643
Teacher spread0.171 · 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
GenreEmpirical

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

Citations1
Published2020
Admission routes1
Has abstractyes

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