Evidence map›Paper›PMID 30455602›Full record

ReviewCost effectiveness and resource allocation : C/E2018

Incorporating MCDA into HTA: challenges and potential solutions, with a focus on lower income settings.

Kevin Marsh, Praveen Thokala, Sitaporn Youngkong, Kalipso Chalkidou

Abstract readReview
In one paragraph

Review in Cost effectiveness and resource allocation : C/E, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  16. Introduction: priority setting in global health.Cost effectiveness and resource allocation : C/E · 2018
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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

4 authors.

Kevin MarshPatient Centred Research, Evidera, London, UK.
Praveen Thokala2ScHARR, University of Sheffield, Sheffield, UK.
Sitaporn Youngkong3Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.
Kalipso Chalkidou4School of Public Health, Imperial College, London, UK.

Funding

Medical Research Council MR/R015600/1
6 · The paper itself

Abstract

backgroundMulticriteria decision analysis (MCDA) has the potential to bring more structure and transparency to health technology assessment (HTA). The objective of this paper is to highlight key methodological and practical challenges facing the use of MCDA for HTA, with a particular focus on lower and middle-income countries (LMICs), and to highlight potential solutions to these challenges. METHODOLOGICAL CHALLENGES: Key lessons from existing applications of MCDA to HTA are summarized, including: that the socio-technical design of the MCDA reflect the local decision problem; the criteria set properties of additive models are understood and applied; and the alternative approaches for estimating opportunity cost, and the challenges with these approaches are understood. PRACTICAL CHALLENGES: Existing efforts to implement HTA in LMICs suggest a number of lessons that can help overcome the practical challenges facing the implementation of MCDA in LMICs, including: adapting inputs from other settings and from expert opinion; investing in technical capacity; embedding the MCDA in the decision-making process; and ensuring that the MCDA design reflects local cultural and social factors.

conclusionMCDA has the potential to improve decision making in LMICs. For this potential to be achieved, it is important that the lessons from existing applications of MCDA are learned.

Indexed as

Health technical assessmentLower and middle-income countriesMulticriteria decision analysis

Identifiers

PMID30455602
PMCPMC6225551

What OpenQuestion holds

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LicenceCC BY
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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.