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Record W2782583025 · doi:10.1017/s0266462317003956

VP155 Synchronization Of Regulatory Approval And Health Technology Assessment Recommendation Timing

2017· article· en· W2782583025 on OpenAlexaboutno aff
Neil McAuslane, Tina Wang, Lawrence Liberti

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Regulatory agencyHealth technologyMedicineDrug approvalBusinessFamily medicineHealth carePolitical sciencePublic administrationPharmacologyDrugLaw

Abstract

fetched live from OpenAlex

INTRODUCTION: Minimizing the delay between regulatory approval and Health Technology Assessment (HTA) recommendation is critical to ensure patients access to medicines of therapeutic value. The aim of this study was to evaluate the level of synchronization between the regulatory decision and HTA recommendation. METHODS: Data were collected from the public domain for new active substances that were first appraised by the HTA agency in Scotland (SMC - Scottish Medicines Consortium), France (HAS - Haute Autorité de Santé), Germany (IQWIG - Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen), Australia (PBAC - Pharmaceutical Benefits Advisory Committee) and Canada (CADTH - Canadian Agency for Drugs and Technologies), and that reached an outcome in 2014 and 2015. The year the product was approved by the European Medicines Agency (EMA), Australian Therapeutic Goods Administration (TGA) and Health Canada were also assessed. RESULTS: In 2014 and 2015, fifty-one products with HTA recommendations were identified for SMC and IQWIG, forty-two for HAS, forty for PBAC and thirty-eight for CADTH. Of the HTA agencies studied, CADTH had the lowest percentage of HTA recommendations occurring the same year as jurisdictional regulatory approval. Of the products with CADTH recommendations in 2014, only 7 percent were approved by Health Canada in the same year. By comparison, all of the products with PBAC recommendations in 2015 were approved by TGA in the same year. For 2014 and 2015, comparing the percentage of HTA recommendations with the jurisdictional regulatory agency approval the same year showed 7 percent (2014) versus 29 percent (2015) for CADTH: 35 percent versus 37 percent for SMC: 35 percent versus 44 percent for HAS; 56 percent versus 57 percent for IQWIG; and 91 percent versus 100 percent for PBAC. CONCLUSIONS: This study shows that the parallel submission mechanism to enable synchronizing HTA and regulatory decision making is effective in Australia, whilst there remains a synchronization disconnect in other countries; although this may be improving. The extent of decision timing disconnect, influence of company strategy and type of HTA outcome were also studied. This initial analysis suggests gaps between the timing of regulatory approval and HTA recommendation for HTA agencies outside of Australia.

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)0.102
metaresearch head score (Gemma)0.312
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.542

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1020.312
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.235
GPT teacher head0.518
Teacher spread0.283 · 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
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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