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Record W2159099688 · doi:10.1586/14737167.2014.868313

Determining the price for pharmaceuticals in Germany: comparing a shortcut for IQWiG's efficiency frontier method with the price set by the manufacturer for ticagrelor

2013· article· en· W2159099688 on OpenAlexaff
Afschin Gandjour, Amiram Gafni, Michael Schlander

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEconomicsTicagrelorSet (abstract data type)FrontierEfficient frontierMicroeconomicsActuarial sciencePublic economicsEconometricsFinancial economicsMedicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Under the recently enacted pharmaceutical price and reimbursement regulation in Germany, manufacturers and payers negotiate an appropriate reimbursement price for new products. If one of the parties involved wishes so, a formal evaluation of costs and benefits will be conducted by the Institute for Quality and Efficiency in Health Care (IQWiG). IQWiG makes recommendations for a reimbursement price based on the 'efficiency frontier' in a therapeutic area. The analysis requires, when applicable, to calculate savings in other areas of the healthcare system (cost offsets) and healthcare costs during the years of life gained (i.e., downstream costs). A recent paper described the conditions under which calculation of downstream costs is not required. The purpose of this study is to use the drug ticagrelor as an example to demonstrate this shortcut for the efficiency frontier method. The analysis shows that applying the IQWiG approach would result in substantial savings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.127
GPT teacher head0.527
Teacher spread0.400 · 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 teacher head, not a consensus.

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".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

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