Evidence map›Paper›PMID 35833636›Full record

SynthesisResearch synthesis methods2023

Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework.

Peter J Godolphin, Ian R White, Jayne F Tierney, David J Fisher

Open access · hybridAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Research synthesis methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 8 pooled it
4.8field-weighted citation impact, top 4% 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

22 citing papers in PubMed, 8 syntheses or guidelines pooled it, 28 citations in OpenAlex.

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  9. Article
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  11. Introduction to Trial-Level Subgroup Analysis: A Tutorial.Cochrane evidence synthesis and methods · 2026
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  20. Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023
    Article
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 1 institution in 1 country.

Peter J GodolphinMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.ORCID https://orcid.org/0000-0003-0648-0992
Ian R WhiteMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.ORCID https://orcid.org/0000-0002-6718-7661
Jayne F TierneyMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.ORCID https://orcid.org/0000-0002-4734-3014
David J FisherMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.ORCID https://orcid.org/0000-0002-2512-2296
MRC Clinical Trials Unit at UCL · GB

Funding

Medical Research Council MC_UU_00004/06National Institute for Health Research's Development and Skills Enhancement Award NIHR301653Prostate Cancer UK RIA 16-ST2-020UK Medical Research Council MC_UU_00004/06
6 · The paper itself

Abstract

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute scales, in order to target treatments appropriately-which estimation of an interaction effect does not in itself provide. Also, the focus has been on covariates with only two subgroups, and may exclude relevant data if only a single subgroup is reported. Therefore, in this article we further develop the "within-trial" framework by providing practical methods to (1) estimate within-trial interactions across two or more subgroups; (2) estimate subgroup-specific ("floating") treatment effects that are compatible with the within-trial interactions and make maximum use of available data; and (3) clearly present this data using novel implementation of forest plots. We described the steps involved and apply the methods to two examples taken from previously published meta-analyses, and demonstrate a straightforward implementation in Stata based upon existing code for multivariate meta-analysis. We discuss how the within-trial framework and plots can be utilised with aggregate (or "published") source data, as well as with individual participant data, to effectively demonstrate how treatment effects differ across participant subgroups.

Indexed as

Research DesignBiasHumanscovariate interactioneffect modifierfloating subgroupmeta-analysissubgroup analysiswithin-trial

Identifiers

PMID35833636
PMCPMC10087172
OpenAlexW4285386704

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.