ReviewCost effectiveness and resource allocation : C/E2020
A systematic review of the budget impact analyses for antitumor drugs of lung cancer.
Review in Cost effectiveness and resource allocation : C/E, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 4 of them syntheses that pooled 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.
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
6 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Budget impact models for lung cancer interventions: A systematic literature review.Journal of managed care & specialty pharmacy · 2024Pooled it
- Markov models for clinical decision-making in radiation oncology: A systematic review.Journal of medical imaging and radiation oncology · 2024Pooled it
- A Systematic Review of Health Economic Evaluations and Budget Impact Analyses to Inform Healthcare Decision-Making in Central America.Applied health economics and health policy · 2023Pooled it
- Is the Scope of Costs Considered in Budget Impact Analyses for Anticancer Drugs Rational? A Systematic Review and Comparative Study.Frontiers in public health · 2021Pooled it
- Budget impact analysis of breast cancer medications: a systematic review.Journal of pharmaceutical policy and practice · 2022Review
- Budget Impact Analysis of Idecabtagene Vicleucel for the Treatment of Adult Patients with Relapsed or Refractory Multiple Myeloma in the US.Clinical drug investigation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundBudget impact analyses (BIAs) are used for reimbursement decisions and drug access medical insurance, as a supplement to cost-effectiveness analyses (CEAs).
objectivesWe systematically reviewed BIAs for antitumor drugs of lung cancer to provide reference for high-value drug budget impact analyses and decision making.
methodsWe conducted a literature search on PubMed, EMbase, The Cochrane Library, China National Knowledge Infrastructure and Wanfang Data Knowledge Service Platform from 2010 to 2019. The methodological indicators and result information of the budget impact analyses were extracted and evaluated for quality.
resultsA total of 14 studies on the budget impact for antitumor drugs of lung cancer were included, and the overall quality was good. Half of studies were from developed countries. Nine of the studies were designed using the BIA cost calculation model, and two were simulated using the Markov model Monte Carlo model. From all studies, only 14.3% reported model validation. The budget impact results of the same drug in different countries were inconsistent.
conclusionsIncluded studies evaluating budget impact analyses for anti-tumor drugs of lung cancer showed variability in the methodological framework for BIAs. The budget impact analyses of high-value drugs need to be more stringent to ensure the accuracy of the parameters, and should provide reliable results based on real data to decision-making departments, which should carefully consider access to lung cancer drugs.
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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.