Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/174677
Title: A Bayesian joint model for zero‐inflated integers and left‐truncated event times with a time‐varying association: Applications to senior health care
Author: Piulachs Lozada-Benavente, Xavier
Andrinopoulou, Eleni-Rosalina
Guillén, Montserrat
Rizopoulos, Dimitris
Keywords: Estadística bayesiana
Mortalitat
Morbiditat
Variables (Matemàtica)
Assistència sanitària
Bayesian statistical decision
Mortality
Morbidity
Variables (Mathematics)
Medical care
Issue Date: 1-Feb-2021
Publisher: John Wiley & Sons
Abstract: Population aging in most industrialized societies has led to a dramatic increase in emergency medical demand among the elderly. In the context of private health care, an optimal allocation of the medical resources for seniors is commonly done by forecasting their life spans. Accounting for each subject's particularities is therefore indispensable, so the available data must be processed at an individual level. We use a large and unique dataset of insured parties aged 65 and older to appropriately relate the emergency care usage with mortality risk. Longitudinal and time‐to‐event processes are jointly modeled, and their underlying relationship can therefore be assessed. Such an application, however, requires some special features to also be considered. First, longitudinal demand for emergency services exhibits a nonnegative integer response with an excess of zeros due to the very nature of the data. These subject‐specific responses are handled by a zero‐inflated version of the hierarchical negative binomial model. Second, event times must account for the left truncation derived from the fact that policyholders must reach the age of 65 before they may begin to be observed. Consequently, a delayed entry bias arises for those individuals entering the study after this age threshold. Third, and as the main challenge of our analysis, the association parameter between both processes is expected to be age‐dependent, with an unspecified association structure. This is well‐approximated through a flexible functional specification provided by penalized B‐splines. The parameter estimation of the joint model is derived under a Bayesian scheme.
Note: Versió postprint del document publicat a: https://doi.org/10.1002/sim.8767
It is part of: Statistics in Medicine, 2021, vol. 40, num. 1, p. 147-166
URI: http://hdl.handle.net/2445/174677
Related resource: https://doi.org/10.1002/sim.8767
ISSN: 0277-6715
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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