IDEAS home Printed from
   My bibliography  Save this article

Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study


  • Rana, Subrata
  • Roy, Surupa
  • Das, Kalyan


In many epidemiological and clinical studies, observations on individuals are recorded longitudinally on a Likert-type scale. In the process of recording, or due to some other causes, a proportion of outcomes and time-dependent covariates may be missing in one or more follow-up visits (non monotone missing). Even when the number of patients with intermittent missing data is small, exclusion of those patients from the study seems unsatisfactory. This apart, often due to misreporting, miscategorization of response can occur that results in potentially invalid inference when no correction is made. We propose a joint mixed model that corrects the likelihood function to account for missing response and/or covariates and adjusts the likelihood to tackle miscategorization of response. Under this extreme complex but useful setup, we seek to estimate the parameters of the proposed model that accounts for baseline and/or time dependent covariates. Monte Carlo expectation–maximization (MCEM) is a convenient approach for estimating the parameters in the model. A simulation study was carried out to assess the approach. We also analyzed Alzheimer’s Disease Neuroimaging Initiative (ADNI) data where some responses and covariates are missing and some responses are possibly miscategorized. Our investigation reveals that apolipo-protein plays a significant role in Alzheimer’s disease progression. This was not visible in earlier analyses of ADNI data.

Suggested Citation

  • Rana, Subrata & Roy, Surupa & Das, Kalyan, 2018. "Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 62-77.
  • Handle: RePEc:eee:jmvana:v:166:y:2018:i:c:p:62-77
    DOI: 10.1016/j.jmva.2018.02.004

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. Amy L. Stubbendick & Joseph G. Ibrahim, 2003. "Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models," Biometrics, The International Biometric Society, vol. 59(4), pages 1140-1150, December.
    2. Chen, Baojiang & Yi, Grace Y. & Cook, Richard J., 2010. "Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 336-353.
    3. Wai-Yin Poon & Hai-Bin Wang, 2010. "Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 498-520, September.
    4. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
    5. Keunbaik Lee & Michael J. Daniels, 2007. "A Class of Markov Models for Longitudinal Ordinal Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1060-1067, December.
    6. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    7. Li C. Liu & Donald Hedeker, 2006. "A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data," Biometrics, The International Biometric Society, vol. 62(1), pages 261-268, March.
    Full references (including those not matched with items on IDEAS)


    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:eee:jmvana:v:166:y:2018:i:c:p:62-77. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.