Evidence map›Paper›PMID 40132816›Full record

ReviewBMJ open2025

Selection of indirect treatment comparisons for health technology assessments: a practical guide for health economics and outcomes research scientists and clinicians.

Jennifer D Guo, Adel Gehchan, Abraham Hartzema

Abstract readReview
In one paragraph

Review in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Trial
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  4. Review
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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

3 authors.

Jennifer D Guo3ARx Solutions LLC, Boston, Massachusetts, USA jennifer.guo@3arxsolutions.com.ORCID http://orcid.org/0009-0008-2230-7384
Adel Gehchan3ARx Solutions LLC, Boston, Massachusetts, USA.ORCID http://orcid.org/0009-0003-5073-2824
Abraham HartzemaUniversity of Florida, Gainesville, Florida, USA.ORCID http://orcid.org/0000-0002-3158-3400

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth technology assessment (HTA) bodies evaluate the clinical and economic values of health interventions to inform healthcare decision-making. They face the challenge of lacking head-to-head randomised clinical trial data against the standard of care. Indirect treatment comparison (ITC) methods are often used and accepted by HTA bodies worldwide, but there are numerous options with various and inconsistent terminologies. The selection and application of ITC methods are complex from methodological and clinical perspectives.

objectivesThis article (1) provides a comprehensive overview of ITC methods by clarifying used terminologies, including fundamental assumptions, frameworks, strengths, limitations, applications and specific considerations; (2) examines recent ITC guidelines with recommendations or preferences from major HTA bodies and (3) guides health economics and outcomes research (HEOR) scientists and clinicians in the strategic selection of ITC methods with case examples.

methodsThe authors conducted a rapid review to identify the literature related to ITC methods and ITC-relevant HTA guidelines in various databases between 2009 and April 2024.

conclusionsComprehensive knowledge of the ITC methods landscape and the evolving ITC-relevant HTA guidelines are essential for ITC methods selection. Effective communication/collaboration between HEOR scientists and clinicians ensures that the selection and justification of ITC methods are robust for HTA submissions.

Indexed as

Economics, MedicalOutcome Assessment, Health CareTechnology Assessment, BiomedicalHumansHealth economicsHealth policyNetwork Meta-AnalysisReview

Identifiers

PMID40132816
PMCPMC11938225

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.