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Record W4312155579 · doi:10.1017/s0266462322001982

PP62 Recommendations On Methodologies To Obtain Comparator Efficacy In Health Economic Assessments Of Tumor-Agnostic Drugs

2022· article· en· W4312155579 on OpenAlexaboutno aff
Reva Efe, Sylvia Vijgen

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsReimbursementHealth technologyContext (archaeology)NiceMedicineExcellenceHealth careRisk analysis (engineering)Computer sciencePolitical science

Abstract

fetched live from OpenAlex

Introduction Guidance on appropriate methods to obtain a comparator arm for the cost-effectiveness analysis of tumor-agnostic drugs is needed. In recent years, multiple tumor-agnostic drugs have been submitted to health technology assessment (HTA) bodies based on data from single-arm basket trials. These target a specific genetic mutation, as opposed to targeting a specific tumor type. Since HTA bodies are interested in the comparative effectiveness of a treatment, manufacturers have used several methods to obtain a synthetic control arm in their submissions. This study provides an overview of the recommendations by HTA bodies on the methodology to obtain comparator efficacy. Methods A targeted literature review will be conducted focusing on the methodology used to obtain a comparator arm in the context of tumor-agnostic drugs. The search will cover key HTA organizations; including the National Institute for Health and Care Excellence (NICE), Haute Autorité de Santé (HAS) and the Canadian Agency for Drugs & Technologies in Health (CADTH). Methodologies used in entrectinib and larotrectinib submissions will be extracted. Particular focus will be given on the impact of the applied methodology to the reimbursement decision, as well as key critiques by the HTA bodies. Key search terms will include the following: ‘tumor-agnostic’, ‘histology independent’, ‘HIT’, ‘entrectinib’, ‘larotrectinib’. Results An overview of the results will be presented. These will include the applied methodology for obtaining a comparator arm, critiques and recommendations from HTA bodies, and the impact these methodologies had on the overall reimbursement decision. This will enable comparison of HTA decision-making across regions, and key evidence gaps that need to be further explored. Conclusions The results of this study could be useful in the future assessment of tumor-agnostic drug submissions, focusing on the methodology used to obtain comparator efficacy.

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)0.222
metaresearch head score (Gemma)0.604
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.222
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2220.604
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.020
Bibliometrics0.0170.019
Science and technology studies0.0020.003
Scholarly communication0.0120.010
Open science0.0100.009
Research integrity0.0140.011
Insufficient payload (model declined to judge)0.0730.035

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 head0.319
GPT teacher head0.568
Teacher spread0.249 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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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Citations0
Published2022
Admission routes1
Has abstractyes

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