A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis.


Journal

Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738

Informations de publication

Date de publication:
Dec 2019
Historique:
received: 28 04 2018
revised: 02 10 2019
accepted: 06 10 2019
pubmed: 5 11 2019
medline: 11 7 2020
entrez: 5 11 2019
Statut: ppublish

Résumé

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.

Identifiants

pubmed: 31682071
doi: 10.1002/jrsm.1382
pmc: PMC6973268
doi:

Substances chimiques

Vitamin D 1406-16-2

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

582-596

Informations de copyright

© 2019 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

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Auteurs

Tat-Thang Vo (TT)

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Université de Paris, CRESS, INSERM, INRA, Paris, France.

Raphael Porcher (R)

Université de Paris, CRESS, INSERM, INRA, Paris, France.

Anna Chaimani (A)

Université de Paris, CRESS, INSERM, INRA, Paris, France.

Stijn Vansteelandt (S)

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

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