Evidence map›Paper›PMID 31709496›Full record

ArticlePharmacoEconomics2020

Development and Validation of the TRansparent Uncertainty ASsessmenT (TRUST) Tool for Assessing Uncertainties in Health Economic Decision Models.

Sabine E Grimm, Xavier Pouwels, Bram L T Ramaekers, Ben Wijnen, Saskia Knies, Janneke Grutters, Manuela A Joore

Abstract read
In one paragraph

Article in PharmacoEconomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. A systematic review of health economic evaluation quality assessment instruments for medical devices.International journal of technology assessment in health care · 2025
    Pooled it
  2. Pooled it
  3. Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Pooled it
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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.

Sabine E GrimmMaastricht University Medical Centre, Department of Clinical Epidemiology and Medical Technology Assessment, School for Public Health and Primary Care (CAPHRI), P. Debyelaan 25, PO Box 5800, 6202 AZ, Maastricht, The Netherlands. Sabine.grimm@mumc.nl.ORCID 0000-0002-2175-7999
Xavier PouwelsMaastricht University Medical Centre, Department of Clinical Epidemiology and Medical Technology Assessment, School for Public Health and Primary Care (CAPHRI), P. Debyelaan 25, PO Box 5800, 6202 AZ, Maastricht, The Netherlands.
Bram L T RamaekersMaastricht University Medical Centre, Department of Clinical Epidemiology and Medical Technology Assessment, School for Public Health and Primary Care (CAPHRI), P. Debyelaan 25, PO Box 5800, 6202 AZ, Maastricht, The Netherlands.
Ben WijnenMaastricht University Medical Centre, Department of Clinical Epidemiology and Medical Technology Assessment, School for Public Health and Primary Care (CAPHRI), P. Debyelaan 25, PO Box 5800, 6202 AZ, Maastricht, The Netherlands.
Saskia KniesZorginstituut Nederland, Willem Dudokhof 1, 1112 ZA, Diemen, The Netherlands.
Janneke GruttersDepartment for Health Evidence, Radboud University Medical Centre, Post 133, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
Manuela A JooreMaastricht University Medical Centre, Department of Clinical Epidemiology and Medical Technology Assessment, School for Public Health and Primary Care (CAPHRI), P. Debyelaan 25, PO Box 5800, 6202 AZ, Maastricht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn increasing number of technologies are obtaining marketing authorisation based on sparse evidence, which causes growing uncertainty and risk within health technology reimbursement decision making. To ensure that uncertainty is considered and addressed within health technology assessment (HTA) recommendations, uncertainties need to be identified, included in health economic models, and reported.

objectiveOur objective was to develop the TRansparent Uncertainty ASsessmenT (TRUST) tool for systematically identifying, assessing, and reporting uncertainties in decision models, with the aim of making uncertainties and their impact on cost effectiveness more explicit and transparent.

methodsTRUST was developed by drawing on the uncertainty and risk assessment literature. To develop and validate this tool, we conducted HTA stakeholder discussion meetings and interviews and applied it in six real-world HTA case studies in the Netherlands and the UK.

resultsThe TRUST tool enables the identification and categorisation of uncertainty according to its source (transparency issues, methodology issues, and issues with evidence: imprecision, bias and indirectness, and unavailability) in each model aspect. The source of uncertainty determines the appropriate analysis. The impact of uncertainties on cost effectiveness is also assessed. Stakeholders found using the tool to be feasible and of value for transparent uncertainty assessment. TRUST can be used during model development and/or model review.

conclusionThe TRUST tool enables systematic identification, assessment, and reporting of uncertainties in health economic models and may contribute to more informed and transparent decision making in the face of uncertainty.

Indexed as

Decision Support TechniquesEconomics, MedicalModels, EconomicCost-Benefit AnalysisHumansNetherlandsTechnology Assessment, BiomedicalUncertaintyUnited Kingdom

Identifiers

PMID31709496
PMCPMC7081657

What OpenQuestion holds

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Registered trials

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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.