Evidence map›Paper›PMID 24944196›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2014

The value of heterogeneity for cost-effectiveness subgroup analysis: conceptual framework and application.

Manuel A Espinoza, Andrea Manca, Karl Claxton, Mark J Sculpher

Open access · hybridAbstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 2 pooled it
21.6field-weighted citation impact, top 1% 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

44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.

  1. A Systematic Review of Economic Evaluations of COVID-19 Interventions: Considerations of Non-Health Impacts and Distributional Issues.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022
    Pooled it
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  4. Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration.The European journal of health economics : HEPAC : health economics in prevention and care · 2019
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  13. Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
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  20. Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
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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 at 2 institutions in 2 countries.

Manuel A EspinozaDepartment of Public Health, Pontificia Universidad Católica de Chile, Santiago, Chile (MAE)
Andrea MancaCentre for Health Economics, University of York, York, UK (AM, KC, MJS)
Karl ClaxtonCentre for Health Economics, University of York, York, UK (AM, KC, MJS)
Mark J SculpherCentre for Health Economics, University of York, York, UK (AM, KC, MJS)
Pontificia Universidad Católica de Chile · CLUniversity of York · GB

Funding

Department of Health CDF-2009-02-21
6 · The paper itself

Abstract

This article develops a general framework to guide the use of subgroup cost-effectiveness analysis for decision making in a collectively funded health system. In doing so, it addresses 2 key policy questions, namely, the identification and selection of subgroups, while distinguishing 2 sources of potential value associated with heterogeneity. These are 1) the value of revealing the factors associated with heterogeneity in costs and outcomes using existing evidence (static value) and 2) the value of acquiring further subgroup-related evidence to resolve the uncertainty given the current understanding of heterogeneity (dynamic value). Consideration of these 2 sources of value can guide subgroup-specific treatment decisions and inform whether further research should be conducted to resolve uncertainty to explain variability in costs and outcomes. We apply the proposed methods to a cost-effectiveness analysis for the management of patients with acute coronary syndrome. This study presents the expected net benefits under current and perfect information when subgroups are defined based on the use and combination of 6 binary covariates. The results of the case study confirm the theoretical expectations. As more subgroups are considered, the marginal net benefit gains obtained under the current information show diminishing marginal returns, and the expected value of perfect information shows a decreasing trend. We present a suggested algorithm that synthesizes the results to guide policy.

Indexed as

Cost-Benefit AnalysisResearchUncertaintycost-effectiveness analysis.heterogeneitysubgroup analysisvalue of information

Identifiers

PMID24944196
PMCPMC4232328
OpenAlexW2158694566

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.