IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i11p1623-d1399437.html
   My bibliography  Save this article

Testing Informativeness of Covariate-Induced Group Sizes in Clustered Data

Author

Listed:
  • Hasika K. Wickrama Senevirathne

    (Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA)

  • Sandipan Dutta

    (Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA)

Abstract

Clustered data are a special type of correlated data where units within a cluster are correlated while units between different clusters are independent. The number of units in a cluster can be associated with that cluster’s outcome. This is called the informative cluster size (ICS), which is known to impact clustered data inference. However, when comparing the outcomes from multiple groups of units in clustered data, investigating ICS may not be enough. This is because the number of units belonging to a particular group in a cluster can be associated with the outcome from that group in that cluster, leading to an informative intra-cluster group size or IICGS. This phenomenon of IICGS can exist even in the absence of ICS. Ignoring the existence of IICGS can result in a biased inference for group-based outcome comparisons in clustered data. In this article, we mathematically formulate the concept of IICGS while distinguishing it from ICS and propose a nonparametric bootstrap-based statistical hypothesis-testing mechanism for testing any claim of IICGS in a clustered data setting. Through simulations and real data applications, we demonstrate that our proposed statistical testing method can accurately identify IICGS, with substantial power, in clustered data.

Suggested Citation

  • Hasika K. Wickrama Senevirathne & Sandipan Dutta, 2024. "Testing Informativeness of Covariate-Induced Group Sizes in Clustered Data," Mathematics, MDPI, vol. 12(11), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1623-:d:1399437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/11/1623/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/11/1623/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Somnath Datta & Glen A. Satten, 2008. "A Signed-Rank Test for Clustered Data," Biometrics, The International Biometric Society, vol. 64(2), pages 501-507, June.
    2. Datta, Somnath & Satten, Glen A., 2005. "Rank-Sum Tests for Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 908-915, September.
    3. Ying Huang & Brian Leroux, 2011. "Informative Cluster Sizes for Subcluster-Level Covariates and Weighted Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 67(3), pages 843-851, September.
    4. Sandipan Dutta & Somnath Datta, 2016. "A rank-sum test for clustered data when the number of subjects in a group within a cluster is informative," Biometrics, The International Biometric Society, vol. 72(2), pages 432-440, June.
    5. John M. Williamson & Somnath Datta & Glen A. Satten, 2003. "Marginal Analyses of Clustered Data When Cluster Size Is Informative," Biometrics, The International Biometric Society, vol. 59(1), pages 36-42, March.
    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. Jaakko Nevalainen & Somnath Datta & Hannu Oja, 2014. "Inference on the marginal distribution of clustered data with informative cluster size," Statistical Papers, Springer, vol. 55(1), pages 71-92, February.
    2. Liya Fu & You-Gan Wang, 2012. "Efficient Estimation for Rank-Based Regression with Clustered Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1074-1082, December.
    3. Sandipan Dutta, 2022. "Robust Testing of Paired Outcomes Incorporating Covariate Effects in Clustered Data with Informative Cluster Size," Stats, MDPI, vol. 5(4), pages 1-13, December.
    4. Omer Ozturk & Asuman Turkmen, 2016. "Quantile inference based on clustered data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(7), pages 867-893, October.
    5. Gelder, Alan & Kovenock, Dan, 2017. "Dynamic behavior and player types in majoritarian multi-battle contests," Games and Economic Behavior, Elsevier, vol. 104(C), pages 444-455.
    6. Somnath Datta & Glen A. Satten, 2008. "A Signed-Rank Test for Clustered Data," Biometrics, The International Biometric Society, vol. 64(2), pages 501-507, June.
    7. You-Gan Wang & Yudong Zhao, 2008. "Weighted Rank Regression for Clustered Data Analysis," Biometrics, The International Biometric Society, vol. 64(1), pages 39-45, March.
    8. Lea Cassar, 2014. "Job mission as a substitute for monetary incentives: experimental evidence," ECON - Working Papers 177, Department of Economics - University of Zurich.
    9. Philippe Aghion & Stefan Bechtold & Lea Cassar & Holger Herz, 2018. "The Causal Effects of Competition on Innovation: Experimental Evidence," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 34(2), pages 162-195.
    10. Jaakko Nevalainen & Denis Larocque & Hannu Oja, 2007. "A weighted spatial median for clustered data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 355-379, February.
    11. Somnath Datta & Jaakko Nevalainen & Hannu Oja, 2012. "A general class of signed-rank tests for clustered data when the cluster size is potentially informative," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 797-808.
    12. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
    13. Haataja, Riina & Larocque, Denis & Nevalainen, Jaakko & Oja, Hannu, 2009. "A weighted multivariate signed-rank test for cluster-correlated data," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1107-1119, July.
    14. Joanna H. Shih & Michael P. Fay, 2017. "Pearson's chi-square test and rank correlation inferences for clustered data," Biometrics, The International Biometric Society, vol. 73(3), pages 822-834, September.
    15. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    16. Jaakko Nevalainen & Denis Larocque & Hannu Oja, 2007. "A weighted spatial median for clustered data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 355-379, February.
    17. Riina Lemponen & Denis Larocque & Jaakko Nevalainen & Hannu Oja, 2012. "Weighted rank tests and Hodges-Lehmann estimates for the multivariate two-sample location problem with clustered data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 977-991, December.
    18. Feng, Jun & Ho, Chun-Yu & Qin, Xiangdong, 2022. "Internal and external reference dependence of incomplete contracts: Experimental evidences," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 189-209.
    19. Maul, D. & Schiereck, D., 2017. "The bond event study methodology since 1974," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 80723, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    20. Philippe Aghion & Ernst Fehr & Richard Holden & Tom Wilkening, 2018. "The Role of Bounded Rationality and Imperfect Information in Subgame Perfect Implementation—An Empirical Investigation," Journal of the European Economic Association, European Economic Association, vol. 16(1), pages 232-274.

    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:12:y:2024:i:11:p:1623-:d:1399437. 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.