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Bayesian models for aggregate and individual patient data component network meta‐analysis
Author(s) -
Efthimiou Orestis,
Seo Michael,
Karyotaki Eirini,
Cuijpers Pim,
Furukawa Toshi A.,
Schwarzer Guido,
Rücker Gerta,
Mavridis Dimitris
Publication year - 2022
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9372
Subject(s) - computer science , component (thermodynamics) , feature selection , aggregate (composite) , bayesian probability , bayesian network , machine learning , data mining , variable (mathematics) , aggregate data , artificial intelligence , statistics , mathematics , mathematical analysis , physics , materials science , composite material , thermodynamics
Abstract 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.