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Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

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  • Nguefack-Tsague, Georges
  • Zucchini, Walter

Abstract

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models.

Suggested Citation

  • Nguefack-Tsague, Georges & Zucchini, Walter, 2011. "Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach," MPRA Paper 28232, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:28232
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    File URL: https://mpra.ub.uni-muenchen.de/28232/1/MPRA_paper_28232.pdf
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    More about this item

    Keywords

    Bayesian networks; hierarchical model; diarrhea infection; disease determinants; logistic regression;

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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