ArticleInternational journal of technology assessment in health care2015
UNDERSTANDING VARIATIONS IN RELATIVE EFFECTIVENESS: A HEALTH PRODUCTION APPROACH.
Article in International journal of technology assessment in health care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 6 citations in OpenAlex.
- Improving Interactions Between Health Technology Assessment Bodies and Regulatory Agencies: A Systematic Review and Cross-Sectional Survey on Processes, Progress, Outcomes, and Challenges.Frontiers in medicine · 2020Review
- RELATIVE EFFECTIVENESS IN BREAST CANCER TREATMENT: A HEALTH PRODUCTION APPROACH.International journal of technology assessment in health care · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRelative effectiveness has become a key concern of health policy. In Europe, this is because of the need for early information to guide reimbursement and funding decisions about new medical technologies. However, ways that effectiveness (does it work?) and efficacy (can it work?) might differ across health systems are poorly understood.
methodsThis study proposes an analytical framework, drawing on production function theory, to systematically identify and quantify the determinants of relative effectiveness and sources of variation between populations and healthcare systems. We consider how methods such as stochastic frontier analysis and data envelopment analysis using a Malmquist productivity index could in principle be used to generate evidence on, and improve understanding about, the sources of variation in relative effectiveness between countries and over time.
resultsBetter evidence on factors driving relative effectiveness could: inform decisions on how to best use a new technology to maximum effectiveness; establish the need if any for follow-up post-launch studies, and provide evidence of the impact of new health technologies on outcomes in different healthcare systems.
conclusionsThe health production function approach for assessment of relative effectiveness is complementary to traditional experimental and observational studies, focusing on identifying, collecting, and analyzing data at the national level, enabling comparisons to take place. There is a strong case for exploring the use of this approach to better understand the impact of new medicines and devices for improvements in health outcomes.
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.