Hidden three-state survival model for bivariate longitudinal count data

Ardo Hout () and Graciela Muniz-Terrera
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Ardo Hout: University College London
Graciela Muniz-Terrera: University of Edinburgh

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 3, No 7, 529-545

Abstract: Abstract A model is presented that describes bivariate longitudinal count data by conditioning on a progressive illness-death process where the two living states are latent. The illness-death process is modelled in continuous time, and the count data are described by a bivariate extension of the binomial distribution. The bivariate distributions for the count data approach include the correlation between two responses even after conditioning on the state. An illustrative data analysis is discussed, where the bivariate data consist of scores on two cognitive tests, and the latent states represent two stages of underlying cognitive function. By including a death state, possible association between cognitive function and the risk of death is accounted for.

Keywords: Bivariate binomial distribution; Markov model; Cognitive function; Stochastic process; Latent-class model (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9448-1

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