ReviewInternational journal of technology assessment in health care2023
Is the quality of evidence in health technology assessment deteriorating over time? A case study on cancer drugs in Australia.
Review in International journal of technology assessment in health care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Realizing the benefits of tumour-agnostic access requires a national health learning system.Nature reviews. Clinical oncology · 2026Article
- HTA responsiveness to today's challenges to health systems: a responsible innovation in health perspective.International journal of technology assessment in health care · 2025Article
- Article
- Evidence of clinical benefit of cancer medicines considered for funding in Australia.International journal of technology assessment in health care · 2024Article
- Bring Out Your Dead: A Review of the Cost Minimisation Approach in Health Technology Assessment Submissions to the Australian Pharmaceutical Benefits Advisory Committee.PharmacoEconomics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to assess whether there have been changes in the quality of clinical evidence submitted for government subsidy decisions on cancer medicines over the past 15 years.
methodsWe reviewed public summary documents (PSDs) reporting on subsidy decisions made by the Pharmaceutical Benefits Advisory Committee (PBAC) from July 2005 to July 2020. Information was extracted on the study design, directness of comparison, sample size, and risk of bias (RoB). Changes in the quality of evidence were assessed using regression analysis.
resultsOverall, 214 PSDs were included in the analysis. Thirty-seven percent lacked direct comparative evidence. Thirteen percent presented observational or single-arm studies as the basis for decisions. Among PSDs presenting indirect comparisons, 78 percent reported transitivity issues. Nearly half (41 percent) of PSDs reporting on medicines supported by head-to-head studies noted there was a moderate/high/unclear RoB. PSDs reporting concerns with RoB increased by a third over the past 7 years, even after adjusting for disease rarity and trial data maturity (OR 1.30, 95% CI: 0.99, 1.70). No time trends were observed regarding the directness of clinical evidence, study design, transitivity issues, or sample size during any of the analyzed periods.
conclusionOur findings indicate that the clinical evidence supplied to inform funding decisions for cancer medicines is often of poor quality and has been deteriorating over time. This is concerning as it introduces greater uncertainty in decision making. This is particularly important as the evidence supplied to the PBAC is often the same as that supplied to other global decision-making bodies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.