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Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification

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  • María José García-Zattera
  • Alejandro Jara
  • Emmanuel Lesaffre
  • Guillermo Marshall

Abstract

Motivated by a longitudinal oral health study, the Signal--Tandmobiel® study, we propose a multivariate binary inhomogeneous Markov model in which unobserved correlated response variables are subject to an unconstrained misclassification process and have a monotone behavior. The multivariate baseline distributions and Markov transition matrices of the unobserved processes are defined as a function of covariates through the specification of compatible full conditional distributions. Distinct misclassification models are discussed. In all cases, the possibility that different examiners were involved in the scoring of the responses of a given subject across time is taken into account. A full Bayesian implementation of the model is described and its performance is evaluated using simulated data. We provide theoretical and empirical evidence that the parameters can be estimated without any external information about the misclassification parameters. Finally, the analyses of the motivating study are presented. Appendices 1--7 are available in the online supplementary materials.

Suggested Citation

  • María José García-Zattera & Alejandro Jara & Emmanuel Lesaffre & Guillermo Marshall, 2012. "Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 976-989, September.
    • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:976-989
    DOI: 10.1080/01621459.2012.682804
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    Cited by:

    1. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & McDonnell, D., 2020. "Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Lizbeth Naranjo & Luz Judith R. Esparza & Carlos J. Pérez, 2020. "A Hidden Markov Model to Address Measurement Errors in Ordinal Response Scale and Non-Decreasing Process," Mathematics, MDPI, vol. 8(4), pages 1-12, April.

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