crossnma-package {crossnma}R Documentation

crossnma: An R package for synthesizing cross-design evidence and cross-format data using Bayesian methods in network meta-analysis and network meta-regression

Description

An R package crossnma for performing (network) meta-analysis and (network) meta-regression (allows including up to 3 covariates) of individual participant data and aggregate data or combination of both. Each format can come from randomized controlled trials or non-randomized studies. Estimates are generated in a Bayesian framework using JAGS. The implemented models are described by Hamza et al. 2022 doi: 10.48550/arXiv.2203.06350.

Details

The evidence in network meta-analysis (NMA) typically comes from randomized controlled trials (RCT) where aggregate data (AD) are extracted from published reports. Retrieving individual participant data (IPD) allows considering participant covariates to explain some of the heterogeneity/inconsistency in the network and identify effect modifiers. Additionally, evidence from non-randomized studies (NRS) reflects the reality in clinical practice and bridges the efficacy-effectiveness gap. The cross-NMA/NMR model is a Bayesian suite for evidence synthesis which extends and integrates four different approaches that combine RCT and NRS evidence into a three-level hierarchical model for the synthesis of IPD and AD. The four approaches account for differences in the design and risk of bias in the RCT and NRS evidence. These four approaches variously ignoring differences in risk of bias, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high risk of bias studies in two different ways.

Further details:

Author(s)

Tasnim Hamza tasnim.hamza@ispm.unibe.ch, Guido Schwarzer sc@imbi.uni-freiburg.de, Georgia Salanti georgia.salanti@ispm.unibe.ch

References

Saramago P, Sutton AJ, Cooper NJ, Manca A (2012): Mixed treatment comparisons using aggregate and individual participant level data. Statistics in Medicine, 10;31(28), 3516-36

Dias, Sofia, N. J. Welton, V. C. C. Marinho, G. Salanti, J.P.T Higgins, and A. E. Ades (2010): Estimation and Adjustment of Bias in Randomized Evidence by Using Mixed Treatment Comparison Meta-Analysis. Journal of the Royal Statistical Society, 173, 613-29

Plummer, Martyn. (2003): JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling.

Tramacere, Del Giovane, I, and G Filippini (2015): Immunomodulators and Immunosuppressants for Relapsing‐remitting Multiple Sclerosis: A Network Meta‐analysis. Cochrane Database of Systematic Reviews, no. 9. John Wiley & Sons, Ltd. doi: 10.1002/14651858.CD011381.pub2.

Verde, Pablo Emilio. (2020): A Bias-Corrected Meta-Analysis Model for Combining, Studies of Different Types and Quality. Biometrical Journal, doi: 10.1002/bimj.201900376


[Package crossnma version 1.0.1 Index]