A Framework for Identifying Treatment-Covariate Interactions in Individual Participant Data Network Meta-Analysis

Autor/inn/en	Freeman, S. C.; Fisher, D.; Tierney, J. F.; Carpenter, J. R.
Titel	A Framework for Identifying Treatment-Covariate Interactions in Individual Participant Data Network Meta-Analysis
Quelle	In: Research Synthesis Methods, 9 (2018) 3, S.393-407 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Freeman, S. C.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1300
Schlagwörter	Medical Research; Patients; Outcomes of Treatment; Meta Analysis; Network Analysis; Cancer; Females; Comparative Analysis; Models; Guidelines; Bias; Evidence; Risk; Research Problems + Suchen Sie Ihr Suchwort? Patient; Meta-analysis; Metaanalyse; Netzplantechnik; Carcinoma; Karzinom; Krebs (med); Krebserkrankung; Weibliches Geschlecht; Analogiemodell; Richtlinien; Evidenz; Risiko; Forschungskritik
Abstract	Background: Stratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCIs). Treatment-covariate interactions contain "within" and "across" trial interactions, where the across-trial interaction is more susceptible to confounding and ecological bias. Methods: We considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare 2 models for NMA with TCIs: (1) 2 effects separating within- and across-trial interactions and (2) a single effect combining within- and across-trial interactions. We argue for a visual assessment of consistency of within- and across-trial interactions and consider more detailed aspects of interaction modelling, eg, common vs trial-specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCIs. Results: Following our framework, we found no evidence in the cervical cancer network for a treatment-stage interaction on the basis of the within-trial interaction. The NMA provided additional power for an across-trial interaction over and above the pairwise evidence. Following our proposed framework, we found that the within- and across-trial interactions should not be combined. Conclusion: Across-trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCIs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01