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Abstract
Standard network metaanalysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. We can relax this assumption if individual patient data (IPD) are available from all studies by fitting an IPD metaregression. However, in many cases IPD are only available from a subset of studies.
In the simplest scenario, IPD are available for an AB study but only AgD for an AC study. Methods such as Matching Adjusted Indirect Comparison (MAIC) create a populationadjusted indirect comparison between treatments B and C. However, the resulting comparison is only valid in the AC population without additional assumptions, and the methods cannot be extended to larger treatment networks. Metaregressionbased approaches can be used in larger networks. However, these typically fit the same model at both the individual and aggregate level which incurs aggregation bias.
We propose a general method for synthesising evidence from individual and aggregate data in networks of all sizes, Multilevel Network MetaRegression, extending the standard NMA framework. An individuallevel regression model is defined, and aggregate study data are fitted by integrating this model over the covariate distributions of the respective studies. Since integration is often complex or even intractable, we take a flexible numerical approach using QuasiMonte Carlo integration, allowing for easy implementation regardless of model form or complexity. Correlation structures between covariates are accounted for using copulae.
We illustrate the method using an example and compare the results to those obtained using current methods. Where heterogeneity may be explained by imbalance in effect modifiers between studies we achieve similar fit to a random effects NMA, but uncertainty is substantially reduced, and the model is more interpretable. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution.
In the simplest scenario, IPD are available for an AB study but only AgD for an AC study. Methods such as Matching Adjusted Indirect Comparison (MAIC) create a populationadjusted indirect comparison between treatments B and C. However, the resulting comparison is only valid in the AC population without additional assumptions, and the methods cannot be extended to larger treatment networks. Metaregressionbased approaches can be used in larger networks. However, these typically fit the same model at both the individual and aggregate level which incurs aggregation bias.
We propose a general method for synthesising evidence from individual and aggregate data in networks of all sizes, Multilevel Network MetaRegression, extending the standard NMA framework. An individuallevel regression model is defined, and aggregate study data are fitted by integrating this model over the covariate distributions of the respective studies. Since integration is often complex or even intractable, we take a flexible numerical approach using QuasiMonte Carlo integration, allowing for easy implementation regardless of model form or complexity. Correlation structures between covariates are accounted for using copulae.
We illustrate the method using an example and compare the results to those obtained using current methods. Where heterogeneity may be explained by imbalance in effect modifiers between studies we achieve similar fit to a random effects NMA, but uncertainty is substantially reduced, and the model is more interpretable. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution.
Original language  English 

Publication status  Unpublished  4 Sept 2018 
Event  Royal Statistical Society 2018 International Conference  City Hall, Cardiff, United Kingdom Duration: 3 Sept 2018 → 6 Sept 2018 https://events.rss.org.uk/rss/frontend/reg/thome.csp?pageID=57555&eventID=194 
Conference
Conference  Royal Statistical Society 2018 International Conference 

Country/Territory  United Kingdom 
City  Cardiff 
Period  3/09/18 → 6/09/18 
Internet address 
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Dive into the research topics of 'Multilevel network metaregression for population adjustment based on individual and aggregate level data'. Together they form a unique fingerprint.Projects
 1 Finished

No Pfizer: Calibration of multiple treatment comparisons using individual patient data
1/03/17 → 29/02/20
Project: Research
Student theses

Calibration of Treatment Effects in Network MetaAnalysis using Individual Patient Data
Author: Phillippo, D. M., 28 Nov 2019Supervisor: Welton, N. (Supervisor) & Dias, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)
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Activities
 1 Participation in conference

Royal Statistical Society 2018 International Conference
David M Phillippo (Speaker)
3 Sept 2018 → 6 Sept 2018Activity: Participating in or organising an event types › Participation in conference