Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2018

Using a Bayesian change-point statistical model with autoregressive terms to study the monthly number of dispensed asthma medications by public health services

  • Identification data

    Identifier: RP:2472
    Handle: https://hdl.handle.net/20.500.11797/RP2472
    Authors:
    Martinez, EdsonSantos Previdelli, Isolde TerezinhaMarques de Mello, LuaneCasale Aragon, DaviMota de Queiroz, José André
    Abstract:
    In this paper, it is proposed a Bayesian analysis of a time series in the presence of a random change-point and autoregressive terms. The development of this model was motivated by a data set related to the monthly number of asthma medications dispensed by the public health services of Ribeirão Preto, Southeast Brazil, from 1999 to 2011. A pronounced increase trend has been observed from 1999 to a specific change-point, with a posterior decrease until the end of the series. In order to obtain estimates for the parameters of interest, a Bayesian Markov Chain Monte Carlo (MCMC) simulation procedure using the Gibbs sampler algorithm was developed. The Bayesian model with autoregressive terms of order 1 fits well to the data, allowing to estimate the change-point at July 2007, and probably reflecting the results of the new health policies and previously adopted programs directed toward patients with asthma. The results imply that the present model is useful to analyse the monthly number of dispensed asthma medications and it can be used to describe a broad range of epidemiological time series data where a change-point is present.

  • Others:

    URV's Author/s: Martinez, Edson Santos Previdelli, Isolde Terezinha Marques de Mello, Luane Casale Aragon, Davi Mota de Queiroz, José André
    Keywords: Time series, regression models, Bayesian methods, change-point model, epidemiological data
    Abstract: In this paper, it is proposed a Bayesian analysis of a time series in the presence of a random change-point and autoregressive terms. The development of this model was motivated by a data set related to the monthly number of asthma medications dispensed by the public health services of Ribeirão Preto, Southeast Brazil, from 1999 to 2011. A pronounced increase trend has been observed from 1999 to a specific change-point, with a posterior decrease until the end of the series. In order to obtain estimates for the parameters of interest, a Bayesian Markov Chain Monte Carlo (MCMC) simulation procedure using the Gibbs sampler algorithm was developed. The Bayesian model with autoregressive terms of order 1 fits well to the data, allowing to estimate the change-point at July 2007, and probably reflecting the results of the new health policies and previously adopted programs directed toward patients with asthma. The results imply that the present model is useful to analyse the monthly number of dispensed asthma medications and it can be used to describe a broad range of epidemiological time series data where a change-point is present.
    Journal publication year: 2018
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Time series, regression models, Bayesian methods, change-point model, epidemiological data

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