Abstract
In the analysis of longitudinal data, it is common to characterize the relationship between (repeated) response measures and the covariates. However, when the covariates do vary over time (time-dependent covariates), there is extra relation due to the delayed effects that need to be accounted for. Moreover, it is not uncommon that these studies consist of simultaneous responses on the subject. However, as the observations are correlated, a joint likelihood function of the simultaneous responses is impossible to afford maximum likelihood estimates. Thus a simultaneous modeling of responses with a working correlation matrix to reflect the hierarchical aspect are presented. Bayesian intervals based on the partitioning of the data matrix is obtained. A demonstration of a fit of a model to Add Heath survey data is given.
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Wilson, J.R., Vazquez-Arreola, E., Chen, (.DG. (2020). Simultaneous Modeling with Time-Dependent Covariates and Bayesian Intervals. In: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-48904-5_7
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DOI: https://doi.org/10.1007/978-3-030-48904-5_7
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