No. 2 (2001)
Articles

Application of time series in medical research

Published September 7, 2001
Mária Fazekasné Kis
Debreceni Egyetem Agrártudományi Centrum, Agrárgazdasági és Vidékfejlesztési Intézet, Agrárinformatikai és Alkalmazott Matematikai Tanszék, Debrecen
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APA

Fazekasné Kis, M. (2001). Application of time series in medical research. Acta Agraria Debreceniensis, (2), 14-22. https://doi.org/10.34101/actaagrar/2/3595

This article reviews the mortality data of chronic liver disease and cirrhosis, as well as tracheal, bronchial and lung cancers, in Hungary by methods of time series analysis. The methods of time series of death rates of chronic liver disease and cirrhosis as well as tracheal, bronchial and lung cancers and their reliability, are analysed from data available from WHO. The author used ARIMA models (autoregressive and integrated moving average models) and auto- and cross-correlation functions to study the substantial role an exogenous environmental factor has on incidences of
death. The confidence intervals of autoregressive (AR) coefficient are compared to the standard normal distribution, the estimation of White’s theory and the continuous time estimation model.
On the basis of the analysis, it may be concluded that chronic liver disease and cirrhosis can be influenced by an exogenous environmental factor, however, this relation cannot be demonstrated for deaths due to tracheal, bronchial or lung cancers. In each case, the continuous estimation of the AR(1) coefficients give the best results.
The paper demonstrates how the presented methods can be applied to agricultural science.

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