ReviewPharmacoEconomics2014
Challenges in modelling the cost effectiveness of various interventions for cardiovascular disease.
Review in PharmacoEconomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using meta-regression analyses in addition to conventional systematic review methods to examine the variation in cost-effectiveness results - a case study.BMC health services research · 2016Pooled it
- Cost-benefit analysis of kidney transplant in patients with chronic kidney disease: a case study in Iran.Cost effectiveness and resource allocation : C/E · 2022Article
- A cost-effectiveness analysis of a community based CVD program in Sweden based on a retrospective register cohort.BMC public health · 2018Article
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
objectivesDecision analytic modelling is essential in performing cost-effectiveness analyses (CEAs) of interventions in cardiovascular disease (CVD). However, modelling inherently poses challenges that need to be dealt with since models always represent a simplification of reality. The aim of this study was to identify and explore the challenges in modelling CVD interventions.
methodsA document analysis was performed of 40 model-based CEAs of CVD interventions published in high-impact journals. We analysed the systematically selected papers to identify challenges per type of intervention (test, non-drug, drug, disease management programme, and public health intervention), and a questionnaire was sent to the corresponding authors to obtain a more thorough overview. Ideas for possible solutions for the challenges were based on the papers, responses, modelling guidelines, and other sources.
resultsThe systematic literature search identified 1,720 potentially relevant articles. Forty authors were identified after screening the most recent 294 papers. Besides the challenge of lack of data, the challenges encountered in the review suggest that it was difficult to obtain a sufficiently valid and accurate cost-effectiveness estimate, mainly due to lack of data or extrapolating from intermediate outcomes. Despite the low response rate of the questionnaire, it confirmed our results.
conclusionsThis combination of a review and a survey showed examples of CVD modelling challenges found in studies published in high-impact journals. Modelling guidelines do not provide sufficient guidance in resolving all challenges. Some of the reported challenges are specific to the type of intervention and disease, while some are independent of intervention and disease.
Indexed as
Identifiers
24748448What 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.