Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Three-step estimation of latent Markov models with covariates and possible dropout
Francesco Bartolucci, Giorgio Eduardo Montanari, Silvia Pandolfi

Last modified: 2013-06-14

Abstract


We illustrate the use of a modified version of the three-step latent class approach in order to estimate a latent Markov model with individual covariates and possible dropout. This approach represents an useful estimation tool when a large number of observed variables and covariates occurs in the model. Motivated by a study on the health status of elderly people hosted in Italian nursing homes, we address the problem to deal with informative missing responses and dropout due to the death of the patient. The proposed model allows us to account for both these types of missingness. We also consider a model in which time-constant and time varying covariates affect the initial and transition probabilities of the latent process, through a suitable parametrization. Aim of the study is to estimate the effect of each nursing home on the probability of transition between latent states, corresponding to different levels of the health status of the patients, and on the probability of dropout.

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