Evidence map›Paper›PMID 42081803›Full record

ArticleEpidemiology (Cambridge, Mass.)2026

Extending Inferences from a Randomized Trial to Trial-eligible and Treatment-candidate Target Populations: Examples of Generalizability and Transportability.

Jessica C Young, Issa J Dahabreh, Stefan James, David Erlinge, Ole Fröbert, Anita Berglund, Rebecca Rylance, Miguel A Hernán, Anthony A Matthews

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Jessica C YoungFrom the Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0003-2655-192
Issa J DahabrehCAUSALab, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Stefan JamesDepartment of Medical Sciences, Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.
David ErlingeDivision of Cardiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Ole FröbertFaculty of Health, Department of Cardiology, Örebro University, Örebro, Sweden.
Anita BerglundFrom the Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Rebecca RylanceDivision of Cardiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Miguel A HernánFrom the Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Anthony A MatthewsFrom the Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Funding

Use of Registries, Claims and Health System Data to Enhance the Evaluation of Cardiovascular Therapies in Clinical TrialsR01HL136708 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Issa J. Dahabreh, Robert Yeh · 2017 to 2026
$6.9M
Methods for generalizing inferences from cluster randomized controlled trials to target populationsR01LM013616 · NLM · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI DAHABREH, ISSA J. · 2022 to 2025
$1.4M
NHLBI NIH HHS R01 HL136708NLM NIH HHS R01 LM013616
6 · The paper itself

Abstract

backgroundWhen decision makers use evidence from a randomized trial to inform population-level decisions, the target population they envision rarely aligns with the population of individuals who enrolled in the trial. Here, we extend inferences from the VALIDATE-SWEDEHEART randomized trial (hereafter, the index trial), which compared the effects of bivalirudin and heparin during percutaneous coronary intervention on the risk of death, reinfarction, and bleeding, to two clinically relevant target populations: first, the trial-eligible population of individuals eligible for the index trial regardless of enrollment, and second, the treatment-candidate population of individuals who are considered candidates for bivalirudin and heparin under routine care, regardless of eligibility for the index trial.

methodsUsing data from the index trial, we fit logistic regression models for the outcome at 180 days in each group based on assigned treatment. We then standardized risk estimates to the baseline covariate distribution of the trial-eligible and treatment-candidate target populations, which were characterized using data from Swedish healthcare registries.

resultsThe estimated risk difference comparing bivalirudin versus heparin was -1.1% (-3.1%, 0.9%) in the trial-eligible population and -1.0% (-3.0%, 1.0%) in the treatment-candidate population. The corresponding risk ratios were 0.92 (0.80, 1.07) and 0.93 (0.80, 1.07), respectively, aligning closely with estimates from the index trial. Absolute risks in each treatment group were, however, between 0.8 and 1.2 percentage points higher in comparison with the index trial.

conclusionsEstimated risk ratios for the broader trial-eligible and treatment-candidate populations generally align with the findings from the index trial. While trials provide essential evidence for healthcare, questions often arise about wider, clinically relevant populations beyond the population of trial participants. By leveraging data from trials and observational data sources, we can attempt to address questions in these wider target populations.

Indexed as

AnticoagulantsAntithrombinsHeparinPatient SelectionPeptide FragmentsPercutaneous Coronary InterventionAgedFemaleHemorrhageHirudinsHumansLogistic ModelsMaleMyocardial InfarctionRandomized Controlled Trials as TopicRecombinant ProteinsAnticoagulantsAntithrombinsbivalirudinHeparinHirudinsPeptide FragmentsRecombinant ProteinsCausal InferenceGeneralizabilityTarget populationTransportability

Identifiers

PMID42081803
PMCPMC13390982

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.