Evidence map›Paper›PMID 26610347›Full record

ArticlePharmacoEconomics2016

What Can We Expect from Value-Based Funding of Medicines? A Retrospective Study.

Anthony Harris, Jing Jing Li, Karen Yong

Abstract read
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In one paragraph

Article in PharmacoEconomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Getting fairer over time? Assessing changes in health technology funding processes using the accountability for reasonableness (A4R) framework.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anthony HarrisCentre for Health Economics, 15 Innovation Walk Monash University, Clayton, VIC, 3800, Australia. anthony.harris@buseco.monash.edu.au.
Jing Jing LiCentre for Health Economics, 15 Innovation Walk Monash University, Clayton, VIC, 3800, Australia.
Karen YongCentre for Health Economics, 15 Innovation Walk Monash University, Clayton, VIC, 3800, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDeciding on public funding for pharmaceuticals on the basis of value for money is now widespread. We suggest that evidence-based assessment of value has restricted the availability of medicines in Australia in a way that reflects the relative bargaining power of government and the pharmaceutical industry. We propose a simple informal game-theoretic model of bargaining between the funding agency and industry and test its predictions using a logistic multiple regression model of past funding decisions made by the Pharmaceutical Benefits Advisory Committee in Australia.

methodThe model estimates the probability of a drug being recommended for subsidy as a function of incremental cost per quality-adjusted life-year (QALY), as well as other drug and market characteristics. Data are major submissions or resubmissions from 1993 to 2009 where there was a claim of superiority and evidence of a difference in quality of life. Independent variables measure the incremental cost per QALY, the cost to the public budget, the strength and quality of the clinical and economic evidence, need as measured by severity of illness and the availability of alternative treatments, whether or not a resubmission, and newspaper reports as a measure of public pressure. We report the odds ratio for each variable and calculate the ratio of the marginal effect of each variable to the marginal effect of the cost per QALY as a measure of the revealed willingness to pay for each of the variables that influence the decision.

resultsThe results are consistent with a bargaining model where a 10,000 Australian dollar ($A) fall in value (increase in cost per QALY) reduces the average probability of public funding from 37 to 33% (95% CI 34-32). If the condition is life threatening or the drug has no active comparator, then the odds of a positive recommendation are 3.18 (95% CI 1.00-10.11) and 2.14 (95% CI 0.95-4.83) greater, equivalent to a $A33,000 and a $A21,000 increase in value (fall in cost per QALY). If both conditions are met, the odds are increased by 4.41 (95% CI 1.28-15.24) times, equivalent to an increase in value of $A46,000. Funding is more likely as time elapses and price falls, but we did not find clear evidence that public or corporate pressure influences decisions.

conclusionEvidence from Australia suggests that the determinants of public funding and pricing decisions for medicines reflect the relative bargaining power of government and drug companies. Value for money depends on the quality of evidence, timing, patient need, perceived benefit and opportunity cost; these factors reflect the potential gains from striking a bargain and the risk of loss from not doing so.

Indexed as

Cost-Benefit AnalysisAustraliaDrug CostsGame TheoryHumansQuality-Adjusted Life YearsRetrospective Studies

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.