IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i3d10.1007_s00180-024-01530-0.html
   My bibliography  Save this article

Robust confidence intervals for meta-regression with interaction effects

Author

Listed:
  • Maria Thurow

    (TU Dortmund University
    UA Ruhr)

  • Thilo Welz

    (TU Dortmund University)

  • Eric Knop

    (TU Dortmund University)

  • Tim Friede

    (Universitätsmedizin Göttingen)

  • Markus Pauly

    (TU Dortmund University
    UA Ruhr)

Abstract

Meta-analysis is an important statistical technique for synthesizing the results of multiple studies regarding the same or closely related research question. So-called meta-regression extends meta-analysis models by accounting for study-level covariates. Mixed-effects meta-regression models provide a powerful tool for evidence synthesis, by appropriately accounting for between-study heterogeneity. In fact, modelling the study effect in terms of random effects and moderators not only allows to examine the impact of the moderators, but often leads to more accurate estimates of the involved parameters. Nevertheless, due to the often small number of studies on a specific research topic, interactions are often neglected in meta-regression. In this work we consider the research questions (i) how moderator interactions influence inference in mixed-effects meta-regression models and (ii) whether some inference methods are more reliable than others. Here we review robust methods for confidence intervals in meta-regression models including interaction effects. These methods are based on the application of robust sandwich estimators of Hartung-Knapp-Sidik-Jonkman (HKSJ) or heteroscedasticity-consistent (HC)-type for estimating the variance-covariance matrix of the vector of model coefficients. Furthermore, we compare different versions of these robust estimators in an extensive simulation study. We thereby investigate coverage and width of seven different confidence intervals under varying conditions. Our simulation study shows that the coverage rates as well as the interval widths of the parameter estimates are only slightly affected by adjustment of the parameters. It also turned out that using the Satterthwaite approximation for the degrees of freedom seems to be advantageous for accurate coverage rates. In addition, different to previous analyses for simpler models, the $$\textbf{HKSJ}$$ HKSJ -estimator shows a worse performance in this more complex setting compared to some of the $$\textbf{HC}$$ HC -estimators.

Suggested Citation

  • Maria Thurow & Thilo Welz & Eric Knop & Tim Friede & Markus Pauly, 2025. "Robust confidence intervals for meta-regression with interaction effects," Computational Statistics, Springer, vol. 40(3), pages 1337-1360, March.
  • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01530-0
    DOI: 10.1007/s00180-024-01530-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01530-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-024-01530-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    2. Zimmermann, Georg & Pauly, Markus & Bathke, Arne C., 2020. "Multivariate analysis of covariance with potentially singular covariance matrices and non-normal responses," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    3. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    4. Elizabeth Tipton & James E. Pustejovsky, 2015. "Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 604-634, December.
    5. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    6. Ian R. White, 2015. "Network meta-analysis," Stata Journal, StataCorp LLC, vol. 15(4), pages 951-985, December.
    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. Welz, Thilo & Viechtbauer, Wolfgang & Pauly, Markus, 2023. "Cluster-robust estimators for multivariate mixed-effects meta-regression," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    2. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    3. Katarzyna Jabłońska, 2018. "Dealing With Heteroskedasticity Within The Modeling Of The Quality Of Life Of Older People," Statistics in Transition New Series, Polish Statistical Association, vol. 19(3), pages 423-452, September.
    4. Cheng, Tsung-Chi, 2012. "On simultaneously identifying outliers and heteroscedasticity without specific form," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2258-2272.
    5. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    6. Benedikt M. Potscher & David Preinerstorfer, 2024. "A Necessary and Sufficient Condition for Size Controllability of Heteroskedasticity Robust Test Statistics," Papers 2412.17470, arXiv.org.
    7. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    8. Francisco Cribari-Neto & Maria da Gloria Lima, 2010. "Approximate inference in heteroskedastic regressions: A numerical evaluation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(4), pages 591-615.
    9. Cyrus J. DiCiccio & Joseph P. Romano & Michael Wolf, 2016. "Improving weighted least squares inference," ECON - Working Papers 232, Department of Economics - University of Zurich, revised Nov 2017.
    10. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    11. repec:jss:jstsof:11:i10 is not listed on IDEAS
    12. Hausman, Jerry & Palmer, Christopher, 2012. "Heteroskedasticity-robust inference in finite samples," Economics Letters, Elsevier, vol. 116(2), pages 232-235.
    13. de Sousa, Maria da Conceição Sampaio & Cribari-Neto, Francisco & Stosic, Borko D., 2005. "Explaining DEA Technical Efficiency Scores in an Outlier Corrected Environment: The Case of Public Services in Brazilian Municipalities," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.
    14. Godfrey, L.G., 2006. "Tests for regression models with heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2715-2733, June.
    15. Michael O'Hara & Christopher F. Parmeter, 2013. "Nonparametric Generalized Least Squares in Applied Regression Analysis," Pacific Economic Review, Wiley Blackwell, vol. 18(4), pages 456-474, October.
    16. Lin, Eric S. & Chou, Ta-Sheng, 2012. "A note on Bayesian interpretations of HCCME-type refinements for nonlinear GMM models," Economics Letters, Elsevier, vol. 116(3), pages 494-497.
    17. repec:jss:jstsof:27:i02 is not listed on IDEAS
    18. Dale Poirier, 2008. "Bayesian Interpretations of Heteroskedastic Consistent Covariance Estimators Using the Informed Bayesian Bootstrap," Working Papers 080905, University of California-Irvine, Department of Economics.
    19. Annalivia Polselli, 2023. "Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples," Papers 2312.17676, arXiv.org.
    20. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    21. Francisco Cribari-Neto & Wilton Silva, 2011. "A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 129-146, June.
    22. Eric S. Lin & Ta-Sheng Chou, 2018. "Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 1-28, January.

    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:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01530-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.