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

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

Abstract

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
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    References listed on IDEAS

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    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.
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