-
Using Bayesian Networks to Model Hierarchical Relationships in Epidemiological Studies
-
Georges Nguefack-Tsague
-
Epidemiol Health. 2011;33:e2011006. Published online June 17, 2011
-
DOI: https://doi.org/10.4178/epih/e2011006
-
-
51,462
View
-
118
Download
-
12
Crossref
-
 Abstract
 PDF
-
Abstract
<sec><title>OBJECTIVES</title><p>To propose an alternative procedure, based on a Bayesian network (BN), for estimation and prediction, and to discuss its usefulness for taking into account the hierarchical relationships among covariates.</p></sec><sec><title>METHODS</title><p>The procedure is illustrated by modeling the risk of diarrhea infection for 2,740 children aged 0 to 59 months in Cameroon. We compare the procedure with a standard logistic regression and with a model based on multi-level logistic regression.</p></sec><sec><title>RESULTS</title><p>The standard logistic regression approach is inadequate, or at least incomplete, in that it does not attempt to account for potentially causal relationships between risk factors. The multi-level logistic regression does model the hierarchical structure, but does so in a piecewise manner; the resulting estimates and interpretations differ from those of the BN approach proposed here. An advantage of the BN approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in any specific state, given the states of the others. The currently available approaches can only predict the outcome (disease), given the states of the covariates.</p></sec><sec><title>CONCLUSION</title><p>A major advantage of BNs is that they can deal with more complex interrelationships between variables whereas competing approaches deal at best only with hierarchical ones. We propose that BN be considered as well as a worthwhile method for summarizing the data in epidemiological studies whose aim is understanding the determinants of diseases and quantifying their effects.</p></sec>
-
Summary
-
Citations
Citations to this article as recorded by  - Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network
Daniel P. Johnson, Vijay Lulla
Frontiers in Public Health.2022;[Epub] CrossRef - Study design synopsis: Battle in the stable: Bayesianism versus Frequentism
Johann Detilleux
Equine Veterinary Journal.2021; 53(2): 199. CrossRef - Learning Bayesian networks from demographic and health survey data
Neville Kenneth Kitson, Anthony C. Constantinou
Journal of Biomedical Informatics.2021; 113: 103588. CrossRef - Novel statistical approaches to identify risk factors for soil-transmitted helminth infection in Timor-Leste
Jessica Yi Han Aw, Naomi E. Clarke, Helen J. Mayfield, Colleen L. Lau, Alice Richardson, Susana Vaz Nery
International Journal for Parasitology.2021; 51(9): 729. CrossRef - Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry
Yang Cao, Mustafa Raoof, Eva Szabo, Johan Ottosson, Ingmar Näslund
Journal of Clinical Medicine.2020; 9(6): 1895. CrossRef - Can dementia be predicted using olfactory identification test in the elderly? A Bayesian network analysis
Ding Ding, Xiaoniu Liang, Zhenxu Xiao, Wanqing Wu, Qianhua Zhao, Yang Cao
Brain and Behavior.2020;[Epub] CrossRef - Development of a multivariable predictive model for postoperative nausea and vomiting after cancer surgery in adults
Léia Alessandra Pinto Yamada, Gabriel Magalhães Nunes Guimarães, Magda Aparecida Santos Silva, Angela Maria Sousa, Hazem Adel Ashmawi
Brazilian Journal of Anesthesiology (English Edition).2019; 69(4): 342. CrossRef - Desenvolvimento de um modelo preditivo multivariado para náusea e vômito no pós‐operatório de cirurgia oncológica em adultos
Léia Alessandra Pinto Yamada, Gabriel Magalhães Nunes Guimarães, Magda Aparecida Santos Silva, Angela Maria Sousa, Hazem Adel Ashmawi
Brazilian Journal of Anesthesiology.2019; 69(4): 342. CrossRef - Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji
Colleen L. Lau, Helen J. Mayfield, John H. Lowry, Conall H. Watson, Mike Kama, Eric J. Nilles, Carl S. Smith
Environmental Modelling & Software.2017; 97: 271. CrossRef - A Mixture-Based Bayesian Model Averaging Method
Georges Nguefack-Tsague, Walter Zucchini
Open Journal of Statistics.2016; 06(02): 220. CrossRef - Effects of Bayesian Model Selection on Frequentist Performances: An Alternative Approach
Georges Nguefack-Tsague, Walter Zucchini
Applied Mathematics.2016; 07(10): 1103. CrossRef - Frequentist Model Averaging and Applications to Bernoulli Trials
Georges Nguefack-Tsague, Walter Zucchini, Siméon Fotso
Open Journal of Statistics.2016; 06(03): 545. CrossRef
|
KSE
