IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v64y2008i3p781-789.html
   My bibliography  Save this article

Inference for Kappas for Longitudinal Study Data: Applications to Sexual Health Research

Author

Listed:
  • Yan Ma
  • Wan Tang
  • Changyong Feng
  • Xin M. Tu

Abstract

Summary Analysis of instrument reliability and rater agreement is used in a wide range of behavioral, medical, psychosocial, and health‐care‐related research to assess psychometric properties of instruments, consensus in disease diagnoses, fidelity of psychosocial intervention, and accuracy of proxy outcomes. For categorical outcomes, Cohen's kappa is the most widely used index of agreement and reliability. In many modern‐day applications, data are often clustered, making inference difficult to perform using existing methods. In addition, as longitudinal study designs become increasingly popular, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this article, we develop a novel approach based on a new class of kappa estimates to tackle the complexities involved in addressing missing data and other related issues arising from a general multirater and longitudinal data setting. The approach is illustrated with real data in sexual health research.

Suggested Citation

  • Yan Ma & Wan Tang & Changyong Feng & Xin M. Tu, 2008. "Inference for Kappas for Longitudinal Study Data: Applications to Sexual Health Research," Biometrics, The International Biometric Society, vol. 64(3), pages 781-789, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:781-789
    DOI: 10.1111/j.1541-0420.2007.00934.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1541-0420.2007.00934.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1541-0420.2007.00934.x?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. Huiman X. Barnhart & Michael Haber & Jingli Song, 2002. "Overall Concordance Correlation Coefficient for Evaluating Agreement Among Multiple Observers," Biometrics, The International Biometric Society, vol. 58(4), pages 1020-1027, December.
    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. Pan Yi & Rose Charles E. & Haber Michael & Ma Yan & Carrasco Josep L. & Stewart Brock & Keitel Wendy A. & Keyserling Harry & Jacobson Robert M. & Poland Gregory & McNeil Michael M., 2013. "Assessing Agreement of Repeated Binary Measurements with an Application to the CDC’s Anthrax Vaccine Clinical Trial," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-14, July.
    2. D. Gunzler & W. Tang & N. Lu & P. Wu & X. Tu, 2014. "A Class of Distribution-Free Models for Longitudinal Mediation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 543-568, October.

    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. Helenowski Irene B & Vonesh Edward F & Demirtas Hakan & Rademaker Alfred W & Ananthanarayanan Vijayalakshmi & Gann Peter H & Jovanovic Borko D, 2011. "Defining Reproducibility Statistics as a Function of the Spatial Covariance Structures in Biomarker Studies," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, January.
    2. Ying Guo & Amita K. Manatunga, 2007. "Nonparametric Estimation of the Concordance Correlation Coefficient under Univariate Censoring," Biometrics, The International Biometric Society, vol. 63(1), pages 164-172, March.
    3. Admassu N. Lamu, 2020. "Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 903-915, August.
    4. John J. Chen & Guangxiang Zhang & Chen Ji & George F. Steinhardt, 2011. "Simple moment-based inferences of generalized concordance correlation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1867-1882, October.
    5. Tsai, Miao-Yu, 2015. "Comparison of concordance correlation coefficient via variance components, generalized estimating equations and weighted approaches with model selection," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 47-58.
    6. Chen, Chia-Cheng & Barnhart, Huiman X., 2008. "Comparison of ICC and CCC for assessing agreement for data without and with replications," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 554-564, December.
    7. Candelaria de la Merced Díaz‐González & Milagros de la Rosa‐Hormiga & Josefa M. Ramal‐López & Juan José González‐Henríquez & María Sandra Marrero‐Morales, 2018. "Factors which influence concordance among measurements obtained by different pulse oximeters currently used in some clinical situations," Journal of Clinical Nursing, John Wiley & Sons, vol. 27(3-4), pages 677-683, February.
    8. Tsai, Miao-Yu & Lin, Chao-Chun, 2018. "Concordance correlation coefficients estimated by variance components for longitudinal normal and Poisson data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 57-70.
    9. Gao, Jingjing & Pan, Yi & Haber, Michael, 2012. "Assessment of observer agreement for matched repeated binary measurements," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1052-1060.
    10. Dennis Mackin & Xenia Fave & Lifei Zhang & Jinzhong Yang & A Kyle Jones & Chaan S Ng & Laurence Court, 2017. "Harmonizing the pixel size in retrospective computed tomography radiomics studies," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-17, September.

    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:biomet:v:64:y:2008:i:3:p:781-789. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.