IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i12p2159-d456651.html
   My bibliography  Save this article

Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach

Author

Listed:
  • Francisco-José Vázquez-Polo

    (Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain)

  • Miguel-Ángel Negrín-Hernández

    (Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain)

  • María Martel-Escobar

    (Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain)

Abstract

In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probabilities. When there are few studies, the number of cluster configurations is manageable, and the meta-inferences can be drawn with BMA techniques. Although this topic has been relatively neglected in the meta-analysis literature, the inference thus obtained accurately reflects the cluster structure of the samples used. In this paper, illustrative examples are given and analysed, using real binary data.

Suggested Citation

  • Francisco-José Vázquez-Polo & Miguel-Ángel Negrín-Hernández & María Martel-Escobar, 2020. "Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach," Mathematics, MDPI, vol. 8(12), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2159-:d:456651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/12/2159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/12/2159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tuyl, Frank & Gerlach, Richard & Mengersen, Kerrie, 2008. "A Comparison of BayesLaplace, Jeffreys, and Other Priors: The Case of Zero Events," The American Statistician, American Statistical Association, vol. 62, pages 40-44, February.
    2. Larose, Daniel T. & Dey, Dipak K., 1998. "Modeling publication bias using weighted distributions in a Bayesian framework," Computational Statistics & Data Analysis, Elsevier, vol. 26(3), pages 279-302, January.
    3. Dulal K. Bhaumik & Anup Amatya & Sharon-Lise T. Normand & Joel Greenhouse & Eloise Kaizar & Brian Neelon & Robert D. Gibbons, 2012. "Meta-Analysis of Rare Binary Adverse Event Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 555-567, June.
    4. Weber, Frank & Knapp, Guido & Ickstadt, Katja & Kundt, Günther & Glass, Anne, 2020. "Zero-cell corrections in random-effects meta-analyses," OSF Preprints qjh5f, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miguel-Angel Negrín-Hernández & María Martel-Escobar & Francisco-José Vázquez-Polo, 2021. "Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    2. Dan Jackson, 2007. "Assessing the Implications of Publication Bias for Two Popular Estimates of between-Study Variance in Meta-Analysis," Biometrics, The International Biometric Society, vol. 63(1), pages 187-193, March.
    3. John Copas & Dan Jackson, 2004. "A Bound for Publication Bias Based on the Fraction of Unpublished Studies," Biometrics, The International Biometric Society, vol. 60(1), pages 146-153, March.
    4. Dankmar Böhning & Heinz Holling & Walailuck Böhning & Patarawan Sangnawakij, 2021. "Investigating heterogeneity in meta-analysis of studies with rare events," METRON, Springer;Sapienza Università di Roma, vol. 79(3), pages 259-272, December.
    5. Maier, Maximilian & VanderWeele, Tyler & Mathur, Maya B, 2021. "Using Selection Models to Assess Sensitivity to Publication Bias: A Tutorial and Call for More Routine Use," MetaArXiv tp45u, Center for Open Science.
    6. 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.
    7. van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2018. "P-uniform," MetaArXiv zqjr9, Center for Open Science.
    8. Ran Etgar & Yuval Cohen, 2022. "Optimizing termination decision for meta-heuristic search techniques that converge to a static objective-value distribution," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 249-271, March.
    9. Soohyun Ahn & Xinlei Wang & Mumu Wang & Johan Lim, 2022. "On continuity correction for RSS-structured cluster randomized designs with binary outcomes," METRON, Springer;Sapienza Università di Roma, vol. 80(3), pages 383-397, December.
    10. Weber, Frank & Knapp, Guido & Glass, Anne & Kundt, Günther & Ickstadt, Katja, 2020. "Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods," OSF Preprints 5zbh6, Center for Open Science.
    11. van Aert, Robbie Cornelis Maria, 2018. "Dissertation R.C.M. van Aert," MetaArXiv eqhjd, Center for Open Science.
    12. Maximilian Maier & Tyler J. VanderWeele & Maya B. Mathur, 2022. "Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use," Campbell Systematic Reviews, John Wiley & Sons, vol. 18(3), September.
    13. Frank Tuyl & Richard Gerlach & Kerrie Mengersen, 2009. "The Rule of Three, its Variants and Extensions," International Statistical Review, International Statistical Institute, vol. 77(2), pages 266-275, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2159-:d:456651. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.