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Using Copulas for Bayesian Meta-analysis

Author

Listed:
  • Savita Jain

    (Panjab University)

  • Suresh K. Sharma

    (Panjab University)

  • Kanchan Jain

    (Panjab University)

Abstract

Specific bivariate classes of distributions with given marginals can be used for contribution of the linking distribution between conditional and unconditional effectiveness using copulas. In this paper, a Bayesian model is proposed for meta-analysis of treatment effectiveness data which are generally discrete Binomial and sparse. A bivariate class of priors is imposed to accommodate a wide range of heterogeneity between the multicenter clinical trials involved in the study. Applications to real data are provided.

Suggested Citation

  • Savita Jain & Suresh K. Sharma & Kanchan Jain, 2022. "Using Copulas for Bayesian Meta-analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 23-41, April.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09312-8
    DOI: 10.1007/s12561-021-09312-8
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    References listed on IDEAS

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    1. I. Bairamov & S. Kotz & M. Bekci, 2001. "New generalized Farlie-Gumbel-Morgenstern distributions and concomitants of order statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(5), pages 521-536.
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    3. Arno Onken & Steffen Grünewälder & Matthias H J Munk & Klaus Obermayer, 2009. "Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-13, November.
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