Analysis of double-peak seasonality in the aetiology of perinatal mortality and childhood acute lymphoblastic leukaemia using the Walter-Elwood method

T. Nyári, K. Virág, R. J.Q. Mcnally

Research output: Article

Abstract

Our study demonstrates the use of the Walter-Elwood method in double-peak seasonal variation. The concept of the geometrical model for analysing cyclic variation is described. Monte Carlo simulation procedures are used to compare the performance of the Walter-Elwood and negative binomial regression methods with double-peak seasonality, in both a comparison between the two methods and a power analysis. The results of 10,000 independent Monte Carlo simulations showed that the Walter-Elwood method and the negative binomial regression analysis identified the same peak in 9,956 samples, indicating that the power of both methods is similar in analysing double-peak cyclic trends. Additionally, two epidemiological applications of double-peak seasonality are presented, which were analysed using the Walter-Elwood method. Further, this is the first study to describe the power of the Walter-Elwood method for double peak seasonality. In conclusion, double-peak seasonality could be investigated with the Walter-Elwood method in ecological studies when only the population at risk is available and there is no other variable.

Original languageEnglish
Pages (from-to)3941-3948
Number of pages8
JournalApplied Ecology and Environmental Research
Volume17
Issue number2
DOIs
Publication statusPublished - jan. 1 2019

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lymphocytic leukemia
etiology
childhood
seasonality
mortality
methodology
leukaemia
analysis
method
at-risk population
simulation
regression analysis
seasonal variation

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Agronomy and Crop Science

Cite this

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title = "Analysis of double-peak seasonality in the aetiology of perinatal mortality and childhood acute lymphoblastic leukaemia using the Walter-Elwood method",
abstract = "Our study demonstrates the use of the Walter-Elwood method in double-peak seasonal variation. The concept of the geometrical model for analysing cyclic variation is described. Monte Carlo simulation procedures are used to compare the performance of the Walter-Elwood and negative binomial regression methods with double-peak seasonality, in both a comparison between the two methods and a power analysis. The results of 10,000 independent Monte Carlo simulations showed that the Walter-Elwood method and the negative binomial regression analysis identified the same peak in 9,956 samples, indicating that the power of both methods is similar in analysing double-peak cyclic trends. Additionally, two epidemiological applications of double-peak seasonality are presented, which were analysed using the Walter-Elwood method. Further, this is the first study to describe the power of the Walter-Elwood method for double peak seasonality. In conclusion, double-peak seasonality could be investigated with the Walter-Elwood method in ecological studies when only the population at risk is available and there is no other variable.",
keywords = "Childhood leukaemia introduction, Cyclic variation, Environmental effect, Geometrical model, Monte-carlo simulations, Perinatal mortality, Population at risk, Power analyses",
author = "T. Ny{\'a}ri and K. Vir{\'a}g and Mcnally, {R. J.Q.}",
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AU - Nyári, T.

AU - Virág, K.

AU - Mcnally, R. J.Q.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Our study demonstrates the use of the Walter-Elwood method in double-peak seasonal variation. The concept of the geometrical model for analysing cyclic variation is described. Monte Carlo simulation procedures are used to compare the performance of the Walter-Elwood and negative binomial regression methods with double-peak seasonality, in both a comparison between the two methods and a power analysis. The results of 10,000 independent Monte Carlo simulations showed that the Walter-Elwood method and the negative binomial regression analysis identified the same peak in 9,956 samples, indicating that the power of both methods is similar in analysing double-peak cyclic trends. Additionally, two epidemiological applications of double-peak seasonality are presented, which were analysed using the Walter-Elwood method. Further, this is the first study to describe the power of the Walter-Elwood method for double peak seasonality. In conclusion, double-peak seasonality could be investigated with the Walter-Elwood method in ecological studies when only the population at risk is available and there is no other variable.

AB - Our study demonstrates the use of the Walter-Elwood method in double-peak seasonal variation. The concept of the geometrical model for analysing cyclic variation is described. Monte Carlo simulation procedures are used to compare the performance of the Walter-Elwood and negative binomial regression methods with double-peak seasonality, in both a comparison between the two methods and a power analysis. The results of 10,000 independent Monte Carlo simulations showed that the Walter-Elwood method and the negative binomial regression analysis identified the same peak in 9,956 samples, indicating that the power of both methods is similar in analysing double-peak cyclic trends. Additionally, two epidemiological applications of double-peak seasonality are presented, which were analysed using the Walter-Elwood method. Further, this is the first study to describe the power of the Walter-Elwood method for double peak seasonality. In conclusion, double-peak seasonality could be investigated with the Walter-Elwood method in ecological studies when only the population at risk is available and there is no other variable.

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KW - Cyclic variation

KW - Environmental effect

KW - Geometrical model

KW - Monte-carlo simulations

KW - Perinatal mortality

KW - Population at risk

KW - Power analyses

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