IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v057c01.html
   My bibliography  Save this article

Criteria to Select a Working Correlation Structure in SAS

Author

Listed:
  • Gosho, Masahiko

Abstract

The generalized estimating equations (GEE) method is popular for analyzing clustered and longitudinal data. It is important to determine a proper working correlation matrix when applying the GEE method since an improper selection sometimes results in inefficient parameter estimates. In this paper, we provide the CriteriaWorkCorr macro in SAS to calculate the criteria proposed by Pan (2001), Hin, Carey, and Wang (2007), Hin and Wang (2009), and Gosho, Hamada, and Yoshimura (2011) for selecting the working correlation structure when the GEE method is applied. We illustrate the implementation and an example of the macro.

Suggested Citation

  • Gosho, Masahiko, 2014. "Criteria to Select a Working Correlation Structure in SAS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(c01).
    • Handle: RePEc:jss:jstsof:v:057:c01
    DOI: http://hdl.handle.net/10.18637/jss.v057.c01
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v057c01/v57c01.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057c01/CriteriaWorkCorr.sas
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v057c01/v57c01.sas
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v057.c01?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. Brajendra C. Sutradhar & Kalyan Das, 2000. "On the Accuracy of Efficiency of Estimating Equation Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 622-625, June.
    2. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    3. Hin, Lin-Yee & Carey, Vincent J. & Wang, You-Gan, 2007. "Criteria for WorkingCorrelationStructure Selection in GEE: Assessment via Simulation," The American Statistician, American Statistical Association, vol. 61, pages 360-364, November.
    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. Donald R. Hoover & Qiuhu Shi & Igor Burstyn & Kathryn Anastos, 2019. "Repeated Measures Regression in Laboratory, Clinical and Environmental Research: Common Misconceptions in the Matter of Different Within- and Between-Subject Slopes," IJERPH, MDPI, vol. 16(3), pages 1-21, February.

    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. María Carmen Pardo & Rosa Alonso, 2019. "Working correlation structure selection in GEE analysis," Statistical Papers, Springer, vol. 60(5), pages 1447-1467, October.
    2. Peng, Cheng & Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2022. "Latent Gaussian copula models for longitudinal binary data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Wang, You-Gan & Hin, Lin-Yee, 2010. "Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3359-3370, December.
    4. Liya Fu & Yangyang Hao & You-Gan Wang, 2018. "Working correlation structure selection in generalized estimating equations," Computational Statistics, Springer, vol. 33(2), pages 983-996, June.
    5. Xu, Jianwen & Wang, You-Gan, 2014. "Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 70-77.
    6. You-Gan Wang & Xu Lin, 2005. "Effects of Variance-Function Misspecification in Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 413-421, June.
    7. Philip M. Westgate & Woodrow W. Burchett, 2017. "A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations," The American Statistician, Taylor & Francis Journals, vol. 71(4), pages 344-353, October.
    8. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    9. Justine Shults & Sarah J. Ratcliffe & Mary Leonard, 2007. "Improved generalized estimating equation analysis via xtqls for quasi-least squares in Stata," Stata Journal, StataCorp LP, vol. 7(2), pages 147-166, June.
    10. Liya Fu & Zhuoran Yang & Yan Zhou & You-Gan Wang, 2021. "An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 679-709, October.
    11. Feng, Sanying & Lian, Heng & Xue, Liugen, 2016. "A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 98-109.
    12. Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.
    13. 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.
    14. You-Gan Wang & Yuning Zhao, 2007. "A Modified Pseudolikelihood Approach for Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 63(3), pages 681-689, September.
    15. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    16. You-Gan Wang & Xu Lin & Min Zhu, 2005. "Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 61(3), pages 684-691, September.
    17. Shuwen Hu & You-Gan Wang & Christopher Drovandi & Taoyun Cao, 2023. "Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 681-711, June.
    18. Fu, Liya & Wang, You-Gan, 2016. "Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 492-502.
    19. Qian, Lianfen & Wang, Suojin, 2017. "Subject-wise empirical likelihood inference in partial linear models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 77-87.
    20. Liya Fu & Zhuoran Yang & Fengjing Cai & You-Gan Wang, 2021. "Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis," Computational Statistics, Springer, vol. 36(2), pages 781-804, June.

    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:jss:jstsof:v:057:c01. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.