Evidence map›Paper›PMID 24438724›Full record

ArticleValue in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research

Use of Bayesian multivariate meta-analysis to estimate the HAQ for mapping onto the EQ-5D questionnaire in rheumatoid arthritis.

Sylwia Bujkiewicz, John R Thompson, Alex J Sutton, Nicola J Cooper, Mark J Harrison, Deborah P M Symmons, Keith R Abrams

Open access · hybridAbstract read
In one paragraph

Article in Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
9.1field-weighted citation impact, top 3% of its field
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

9 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Classifying information-sharing methods.BMC medical research methodology · 2021
    Article
  4. Bayesian Multiparameter Evidence Synthesis to Inform Decision Making: A Case Study in Metastatic Hormone-Refractory Prostate Cancer.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    Article
  5. Article
  6. Article
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  8. Review
  9. Review
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

7 authors at 2 institutions in 1 country.

Sylwia BujkiewiczBiostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK. Electronic address: sb309@le.ac.uk.
John R ThompsonGenetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Alex J SuttonBiostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Nicola J CooperBiostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
Mark J HarrisonManchester Centre for Health Economics, Institute of Population Health, The University of Manchester, Manchester, UK.
Deborah P M SymmonsNIHR Manchester Musculoskeletal Biomedical Research Unit, Arthritis Research UK Epidemiology Unit, School of Translational Medicine, University of Manchester, Manchester, UK.
Keith R AbramsBiostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
University of Leicester · GBUniversity of Manchester · GB

Funding

Arthritis Research UK 20380Department of Health MANMKBRU-2012-1Medical Research Council G0800770
6 · The paper itself

Abstract

backgroundIn health technology assessment, decisions about reimbursement for new health technologies are largely based on effectiveness estimates. Sometimes, however, the target effectiveness estimates are not readily available. This may be because many alternative instruments measuring these outcomes are being used (and not all always reported) or an extended follow-up time of clinical trials is needed to evaluate long-term end points, leading to the limited data on the target clinical outcome. In the areas of highest priority in health care, decisions are required to be made on a short time scale. Therefore, alternative clinical outcomes, including surrogate end points, are increasingly being considered for use in evidence synthesis as part of economic evaluation.

objectiveTo illustrate the potential effect of reduced uncertainty around the clinical outcome on the utility when estimating it from a multivariate meta-analysis.

methodsBayesian multivariate meta-analysis has been used to synthesize data on correlated outcomes in rheumatoid arthritis and to incorporate external data in the model in the form of informative prior distributions. Estimates of Health Assessment Questionnaire were then mapped onto the health-related quality-of-life measure EuroQol five-dimensional questionnaire, and the effect was compared with mapping the Health Assessment Questionnaire obtained from the univariate approach.

resultsThe use of multivariate meta-analysis can lead to reduced uncertainty around the effectiveness parameter and ultimately uncertainty around the utility.

conclusionsBy allowing all the relevant data to be incorporated in estimating clinical effectiveness outcomes, multivariate meta-analysis can improve the estimation of health utilities estimated through mapping methods. While reduced uncertainty may have an effect on decisions based on economic evaluation of new health technologies, the use of short-term surrogate end points can allow for early decisions. More research is needed to determine the circumstances under which uncertainty is reduced.

Indexed as

Meta-Analysis as TopicQuality of LifeTechnology Assessment, BiomedicalAntirheumatic AgentsArthritis, RheumatoidBayes TheoremHumansSurveys and QuestionnairesAntirheumatic AgentsBayesian analysishealth technology assessmentmeta-analysismultiple end pointsrheumatoid arthritissurrogate end points

Identifiers

PMID24438724
PMCPMC3919215
OpenAlexW1980749635

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.