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Bayesian estimation and prediction for network meta-analysis with contrast-based approach

  • Hisashi Noma ORCID logo EMAIL logo
Published/Copyright: July 4, 2023
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The International Journal of Biostatistics
From the journal The International Journal of Biostatistics Volume 20 Issue 2

Abstract

Network meta-analysis is gaining prominence in clinical epidemiology and health technology assessments that enable comprehensive assessment of comparative effectiveness for multiple available treatments. In network meta-analysis, Bayesian methods have been one of the standard approaches for the arm-based approach and are widely applied in practical data analyses. Also, for most cases in these applications, proper noninformative priors are adopted, which does not incorporate subjective prior knowledge into the analyses, and reference Bayesian analyses are major choices. In this article, we provide generic Bayesian analysis methods for the contrast-based approach of network meta-analysis, where the generic Bayesian methods can treat proper and improper prior distributions. The proposed methods enable direct sampling from the posterior and posterior predictive distributions without formal iterative computations such as Markov chain Monte Carlo, and technical convergence checks are not required. In addition, representative noninformative priors that can be treated in the proposed framework involving the Jeffreys prior are provided. We also provide an easy-to-handle R statistical package, BANMA, to implement these Bayesian analyses via simple commands. The proposed Bayesian methods are illustrated using various noninformative priors through applications to two real network meta-analyses.

Keywords: Bayesian analysis; contrast-based approach; improper prior; network meta-analysis; noninformative prior
Corresponding author: Hisashi Noma, Department of Data Science, The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan, E-mail:

Funding source: Japan Society for the Promotion of Science

Award Identifier / Grant number: JP22H03554, JP22K19688

  1. Author contributions: The author has responsibility for the entire content of this submitted manuscript.

  2. Research funding: This study was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (grant numbers: JP22H03554, JP22K19688).

  3. Conflict of interest statement: The author declares no conflicts of interest regarding this article.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijb-2022-0121).

Received: 2022-09-29
Accepted: 2023-06-19
Published Online: 2023-07-04

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijb-2022-0121
Keywords for this article
Bayesian analysis; contrast-based approach; improper prior; network meta-analysis; noninformative prior

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Random forests for survival data: which methods work best and under what conditions?
  4. Flexible variable selection in the presence of missing data
  5. An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma
  6. MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination
  7. Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
  8. Hypothesis testing for detecting outlier evaluators
  9. Response to comments on ‘sensitivity of estimands in clinical trials with imperfect compliance’
  10. Commentary
  11. Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan
  12. Research Articles
  13. Optimizing personalized treatments for targeted patient populations across multiple domains
  14. Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
  15. History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome
  16. Revisiting incidence rates comparison under right censorship
  17. Ensemble learning methods of inference for spatially stratified infectious disease systems
  18. The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications
  19. Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials
  20. Estimation of a decreasing mean residual life based on ranked set sampling with an application to survival analysis
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  23. A modified rule of three for the one-sided binomial confidence interval
  24. Kalman filter with impulse noised outliers: a robust sequential algorithm to filter data with a large number of outliers
  25. Bayesian estimation and prediction for network meta-analysis with contrast-based approach
  26. Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods
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