IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v67y2018i5p1177-1205.html
   My bibliography  Save this article

The role of secondary outcomes in multivariate meta‐analysis

Author

Listed:
  • John B. Copas
  • Dan Jackson
  • Ian R. White
  • Richard D. Riley

Abstract

Univariate meta‐analysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate meta‐analysis allows us to take these secondary outcomes into account and can also include studies where the primary outcome is missing. We define the efficiency E as the variance of the overall estimate from a multivariate meta‐analysis relative to the variance of the overall estimate from a univariate meta‐analysis. The extra information gained from a multivariate meta‐analysis of n studies is then similar to the extra information gained if a univariate meta‐analysis of the primary effect had a further n(1−E)/E studies. The variance contribution of a study's secondary outcomes (its borrowing of strength) can be thought of as a contrast between the variance matrix of the outcomes in that study and the set of variance matrices of all the studies in the meta‐analysis. In the bivariate case this is given a simple graphical interpretation as the borrowing‐of‐strength plot. We discuss how these findings can also be used in the context of random‐effects meta‐analysis. Our discussion is motivated by a published meta‐analysis of 10 antihypertension clinical trials.

Suggested Citation

  • John B. Copas & Dan Jackson & Ian R. White & Richard D. Riley, 2018. "The role of secondary outcomes in multivariate meta‐analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1177-1205, November.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1177-1205
    DOI: 10.1111/rssc.12274
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12274
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John Copas & Claudia Lozada‐Can, 2009. "The radial plot in meta‐analysis: approximations and applications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(3), pages 329-344, July.
    2. Han Chen & Alisa K. Manning & Josée Dupuis, 2012. "A Method of Moments Estimator for Random Effect Multivariate Meta-Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1278-1284, December.
    3. Richard D. Riley, 2009. "Multivariate meta‐analysis: the effect of ignoring within‐study correlation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(4), pages 789-811, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Manpreet Blessin & Sophie Lehmann & Angela M. Kunzler & Rolf van Dick & Klaus Lieb, 2022. "Resilience Interventions Conducted in Western and Eastern Countries—A Systematic Review," IJERPH, MDPI, vol. 19(11), pages 1-25, June.

    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. Ito, Tsubasa & Sugasawa, Shonosuke, 2021. "Improved confidence regions in meta-analysis of diagnostic test accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    2. Freling, Traci H. & Vincent, Leslie H. & Henard, David H., 2014. "When not to accentuate the positive: Re-examining valence effects in attribute framing," Organizational Behavior and Human Decision Processes, Elsevier, vol. 124(2), pages 95-109.
    3. Hui Yao & Sungduk Kim & Ming-Hui Chen & Joseph G. Ibrahim & Arvind K. Shah & Jianxin Lin, 2015. "Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 528-544, June.
    4. Dan Jackson & Sylwia Bujkiewicz & Martin Law & Richard D. Riley & Ian R. White, 2018. "A matrix†based method of moments for fitting multivariate network meta†analysis models with multiple outcomes and random inconsistency effects," Biometrics, The International Biometric Society, vol. 74(2), pages 548-556, June.
    5. Stanley, T. D. & Doucouliagos, Hristos, 2013. "Better than random: weighted least squares meta-regression analysis," Working Papers eco_2013_2, Deakin University, Department of Economics.
    6. Dan Jackson, 2018. "Discussion on Quantifying publication bias in meta‐analysis," Biometrics, The International Biometric Society, vol. 74(3), pages 795-796, September.
    7. Jerome Geyer-Klingeberg & Markus Hang & Andreas W. Rathgeber & Stefan Stöckl & Matthias Walter, 2018. "What do we really know about corporate hedging? A meta-analytical study," Business Research, Springer;German Academic Association for Business Research, vol. 11(1), pages 1-31, February.
    8. Bodnar, Olha & Bodnar, Taras, 2024. "Gibbs sampler approach for objective Bayesian inference in elliptical multivariate meta-analysis random effects model," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    9. Elena Kulinskaya & Stephan Morgenthaler & Robert G. Staudte, 2014. "Combining Statistical Evidence," International Statistical Review, International Statistical Institute, vol. 82(2), pages 214-242, August.
    10. Bodnar, Olha & Bodnar, Taras, 2021. "Objective Bayesian meta-analysis based on generalized multivariate random effects model," Working Papers 2021:5, Örebro University, School of Business.
    11. Shi, Chengchun & Song, Rui & Lu, Wenbin & Fu, Bo, 2018. "Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects," LSE Research Online Documents on Economics 102112, London School of Economics and Political Science, LSE Library.
    12. Sohail Akhtar & Jamal Abdul Nasir & Muhammad Usman & Aqsa Sarwar & Rizwana Majeed & Baki Billah, 2020. "The prevalence of hepatitis C virus in hemodialysis patients in Pakistan: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-13, May.
    13. Ian R. White, 2011. "Multivariate random-effects meta-regression: Updates to mvmeta," Stata Journal, StataCorp LP, vol. 11(2), pages 255-270, June.
    14. Blakeley B. McShane & Ulf Böckenholt, 2018. "Multilevel Multivariate Meta-analysis with Application to Choice Overload," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 255-271, March.
    15. Pia Abel zur Wiesch & Roger Kouyos & Sören Abel & Wolfgang Viechtbauer & Sebastian Bonhoeffer, 2014. "Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models," PLOS Pathogens, Public Library of Science, vol. 10(6), pages 1-13, June.
    16. Rebecca Graziani & Sergio Venturini, 2020. "A Bayesian approach to discrete multiple outcome network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssc:v:67:y:2018:i:5:p:1177-1205. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.