Evidence map›Paper›PMID 32350891›Full record

ArticleStatistics in medicine2020

Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning.

Richard D Riley, Thomas P A Debray, David Fisher, Miriam Hattle, Nadine Marlin, Jeroen Hoogland, Francois Gueyffier, Jan A Staessen, Jiguang Wang, Karel G M Moons and 2 more

Abstract read
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In one paragraph

Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 50 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed, 50 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

100 citing papers in PubMed, 50 syntheses or guidelines pooled it.

  1. Clinically effective child change in family-based behavioral treatment for pediatric obesity: An individual participant mega-analysis.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2026
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40 more citing papers are in PubMed but not listed here.

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

12 authors.

Richard D RileyCentre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.ORCID 0000-0001-8699-0735
Thomas P A DebrayJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-1790-2719
David FisherMRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences, University College London, London, UK.
Miriam HattleCentre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.
Nadine MarlinBlizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Jeroen HooglandJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-2397-6052
Francois GueyffierInserm, Lyon, France.
Jan A StaessenDepartment of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, Studies Coordinating Centre, KU Leuven, Leuven, Belgium.
Jiguang WangCentre for Epidemiological Studies and Clinical Trials, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Karel G M MoonsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Johannes B ReitsmaJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Joie EnsorCentre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.ORCID 0000-0001-7481-0282

Funding

Department of Health DRF-2018-11-ST2-077Medical Research Council MC_UU_12023/21
6 · The paper itself

Abstract

Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant-level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment-covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta-analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta-analysis of randomized trials to examine treatment-covariate interactions. For conduct, two-stage and one-stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta-analysis results for subgroups; (ii) interaction estimates should be based solely on within-study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta-analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta-analysis project should not be based on between-study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta-analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta-analysis projects are used for illustration throughout.

Indexed as

Data AnalysisModels, StatisticalHumansMeta-Analysis as TopicProportional Hazards Modelseffect modifierindividual participant data (IPD)meta-analysissubgroup effecttreatment-covariate interaction

Identifiers

PMID32350891
PMCPMC7401032

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.