Bayesian models for aggregate and individual patient data component network meta-analysis.

Efthimiou, Orestis; Seo, Michael; Karyotaki, Eirini; Cuijpers, Pim; Furukawa, Toshi A; Schwarzer, Guido; Rücker, Gerta; Mavridis, Dimitris (2022). Bayesian models for aggregate and individual patient data component network meta-analysis. Statistics in medicine, 41(14), pp. 2586-2601. Wiley-Blackwell 10.1002/sim.9372

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Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)
04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM)

Graduate School:

Graduate School for Health Sciences (GHS)

UniBE Contributor:

Efthimiou, Orestis, Seo, Michael Juhn Uh

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0277-6715

Publisher:

Wiley-Blackwell

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Pubmed Import

Date Deposited:

11 Mar 2022 11:40

Last Modified:

05 Dec 2022 16:14

Publisher DOI:

10.1002/sim.9372

PubMed ID:

35261053

Uncontrolled Keywords:

complex interventions composite model selection multiple treatments

BORIS DOI:

10.48350/167236

URI:

https://boris.unibe.ch/id/eprint/167236

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