IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v44y2017i11p1920-1937.html
   My bibliography  Save this article

Joint GEEs for multivariate correlated data with incomplete binary outcomes

Author

Listed:
  • G. Inan
  • R. Yucel

Abstract

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.

Suggested Citation

  • G. Inan & R. Yucel, 2017. "Joint GEEs for multivariate correlated data with incomplete binary outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1920-1937, August.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1920-1937
    DOI: 10.1080/02664763.2016.1238049
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1238049
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1238049?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. Chung-Wei Shen & Yi-Hau Chen, 2012. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness," Biometrics, The International Biometric Society, vol. 68(4), pages 1046-1054, December.
    2. Lee, Donghwan & Lee, Youngjo & Paik, Myunghee Cho & Kenward, Michael G., 2013. "Robust inference using hierarchical likelihood approach for heavy-tailed longitudinal outcomes with missing data: An alternative to inverse probability weighted generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 171-179.
    3. Steffen Fieuws & Geert Verbeke & Filip Boen & Christophe Delecluse, 2006. "High dimensional multivariate mixed models for binary questionnaire data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 449-460, August.
    4. Malay Naskar & Kalyan Das, 2006. "Semiparametric Analysis of Two-Level Bivariate Binary Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1004-1013, December.
    5. Beunckens, Caroline & Sotto, Cristina & Molenberghs, Geert, 2008. "A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1533-1548, January.
    6. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Debajyoti Sinha & Michael Parzen & Steven Lipshultz, 2009. "Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 3-20, January.
    7. Yucel, Recai M. & He, Yulei & Zaslavsky, Alan M., 2008. "Using Calibration to Improve Rounding in Imputation," The American Statistician, American Statistical Association, vol. 62, pages 125-129, May.
    8. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    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. Jeonghye Choi & David R. Bell & Leonard M. Lodish, 2012. "Traditional and IS-Enabled Customer Acquisition on the Internet," Management Science, INFORMS, vol. 58(4), pages 754-769, April.
    2. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    3. Alexandra Grand & Regina Dittrich & Brian Francis, 2015. "Markov models of dependence in longitudinal paired comparisons: an application to course design," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 237-257, April.
    4. Margaux Delporte & Steffen Fieuws & Geert Molenberghs & Geert Verbeke & Simeon Situma Wanyama & Elpis Hatziagorou & Christiane De Boeck, 2022. "A joint normal‐binary (probit) model," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 37-51, December.
    5. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Roger D. Weiss, 2020. "Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 890-904, December.
    6. Chixiang Chen & Biyi Shen & Aiyi Liu & Rongling Wu & Ming Wang, 2021. "A multiple robust propensity score method for longitudinal analysis with intermittent missing data," Biometrics, The International Biometric Society, vol. 77(2), pages 519-532, June.
    7. Bruce J. Swihart & Brian S. Caffo & Ciprian M. Crainiceanu, 2014. "A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes," International Statistical Review, International Statistical Institute, vol. 82(2), pages 275-295, August.
    8. Melanie Prague & Rui Wang & Alisa Stephens & Eric Tchetgen Tchetgen & Victor DeGruttola, 2016. "Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes," Biometrics, The International Biometric Society, vol. 72(4), pages 1066-1077, December.
    9. Anna Ivanova & Geert Molenberghs & Geert Verbeke, 2017. "Mechanism for missing data incorporated in joint modelling of ordinal responses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 1049-1064, November.
    10. Ângela Jornada Ben & Johanna M. Dongen & Mohamed El Alili & Martijn W. Heymans & Jos W. R. Twisk & Janet L. MacNeil-Vroomen & Maartje Wit & Susan E. M. Dijk & Teddy Oosterhuis & Judith E. Bosmans, 2023. "The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(6), pages 951-965, August.
    11. Shinpei Imori, 2015. "Model Selection Criterion Based on the Multivariate Quasi-Likelihood for Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1214-1224, December.
    12. Clemontina A. Davenport & Arnab Maity & Patrick F. Sullivan & Jung-Ying Tzeng, 2018. "A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 117-138, April.
    13. Manuel Gomes & Edmond S.-W. Ng & Richard Grieve & Richard Nixon & James Carpenter & Simon G. Thompson, 2012. "Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials," Medical Decision Making, , vol. 32(2), pages 350-361, March.
    14. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    15. Eliseth Ribeiro Leão & Daniela Reis Dal Fabbro & Rebeca Barqueiro de Oliveira & Ingrid Ribeiro dos Santos & Elivane da Silva Victor & Rita Lacerda Aquarone & Cristiane Benvenuto Andrade & Vivian Finot, 2017. "Stress, self-esteem and well-being among female health professionals: A randomized clinical trial on the impact of a self-care intervention mediated by the senses," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
    16. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    17. Yan Xia & Yanyun Yang, 2016. "Bias Introduced by Rounding in Multiple Imputation for Ordered Categorical Variables," The American Statistician, Taylor & Francis Journals, vol. 70(4), pages 358-364, October.
    18. Arnab Mukherji & Satrajit Roychowdhury & Pulak Ghosh & Sarah Brown, 2012. "Estimating Healthcare Demand for an Aging Population: A Flexible and Robust Bayesian Joint Model," Working Papers 2012027, The University of Sheffield, Department of Economics.
    19. Yucel, Recai M. & Demirtas, Hakan, 2010. "Impact of non-normal random effects on inference by multiple imputation: A simulation assessment," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 790-801, March.
    20. Thomas Suesse & Ivy Liu, 2013. "Modelling Strategies for Repeated Multiple Response Data," International Statistical Review, International Statistical Institute, vol. 81(2), pages 230-248, August.

    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:taf:japsta:v:44:y:2017:i:11:p:1920-1937. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.