Bayesian Random-effect Model for Predicting Outcome Fraught with Heterogeneity

Yen, Amy Ming Fang; Liou, Hung-Hsiang; Lin, Han; Chen, Chien-Jen

doi:10.1055/s-0038-1634127

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…By using the Bayesian hierarchical model, our approach extended Becker's linear logistic model, which takes into account the correlations between observed cases in epidemic data across households while preserving the mechanisms of disease transmission within the model. The Bayesian hierarchical model has been used to cope with the heterogeneity found in longitudinal follow-up studies characterised by multilevel structures [30][31][32]. The flexible framework of the Bayesian hierarchical approach makes it feasible to model data with complex structures.…”

Section: Discussionmentioning

confidence: 99%

“…To address the multilevel data structure, several statistical methods have been proposed, including the individual-specific random effects model [27,28] and the population-based average method [29]. The former, using the Bayesian DAG model, has been widely used in the literature [30][31][32]. Details of the DAG model have been described by Spiegelhalter et al [33].…”

Section: Bayesian Acyclic Graphical Model For Multilevel Datamentioning

confidence: 99%

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Analysis of household data on influenza epidemic with Bayesian hierarchical model

Hsu

Yen

Chen

et al. 2015

Mathematical Biosciences

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Data used for modelling the household transmission of infectious diseases, such as influenza, have inherent multilevel structures and correlated property, which make the widely used conventional infectious disease transmission models (including the Greenwood model and the Reed-Frost model) not directly applicable within the context of a household (due to the crowded domestic condition or socioeconomic status of the household). Thus, at the household level, the effects resulting from individual-level factors, such as vaccination, may be confounded or modified in some way. We proposed the Bayesian hierarchical random-effects (random intercepts and random slopes) model under the context of generalised linear model to capture heterogeneity and variation on the individual, generation, and household levels. It was applied to empirical surveillance data on the influenza epidemic in Taiwan. The parameters of interest were estimated by using the Markov chain Monte Carlo method in conjunction with the Bayesian directed acyclic graphical models. Comparisons between models were made using the deviance information criterion. Based on the result of the random-slope Bayesian hierarchical method under the context of the Reed-Frost transmission model, the regression coefficient regarding the protective effect of vaccination varied statistically significantly from household to household. The result of such a heterogeneity was robust to the use of different prior distributions (including non-informative, sceptical, and enthusiastic ones). By integrating out the uncertainty of the parameters of the posterior distribution, the predictive distribution was computed to forecast the number of influenza cases allowing for random-household effect.

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Section: Discussionmentioning

confidence: 99%

Section: Bayesian Acyclic Graphical Model For Multilevel Datamentioning

confidence: 99%

Analysis of household data on influenza epidemic with Bayesian hierarchical model

Hsu

Yen

Chen

et al. 2015

Mathematical Biosciences

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“…For the nationwide survey, we first reported the distribution of sextant‐level PD measured by the participating trained dentists by personal characteristics, including gender, age, education level, body mass index (BMI), type 2 diabetes mellitus (DM) and lifestyle factors such as cigarette smoking and alcohol consumption. To take into account the correlated property of sextant‐level data from the same subject or the same dentist, we applied a Bayesian hierarchical model with the incorporation of correlated properties (Yen, Liou, Lin, & Chen, 2006) and measurement errors to estimate the calibrated odds ratio between risk factors and PD; we applied this hierarchical univariate logistic regression model with a random intercept, accounting for different baselines in the same cluster, to estimate the crude odds ratio (cOR) for the effect of each risk factor on PD. The random intercept term was assumed to follow a normal distribution centered at zero with a standard deviation, denoted by σ, which was used to test whether the random effect is statistically significant.…”

Section: Methodsmentioning

confidence: 99%

Effects of risk factors on periodontal disease defined by calibrated community periodontal index and loss of attachment scores

Yen

Lai

et al. 2017

Oral Diseases

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ObjectivesWe evaluated whether and how the effects of risk factors on periodontal disease (PD) were modified by measurement errors using community periodontal index (CPI) and loss attachment (LA) in the community‐based survey.MethodsA pilot validation study was performed to estimate the rates of false negative and false positive for both CPI and LA in 31 subjects from different regions using measurements from 12 well‐trained dentists and a senior periodontist as a gold standard. Afterward, a Taiwanese nationwide survey was conducted by enrolling 3,860 participants to estimate the effect of each risk factor on PD calibrated with both sensitivity and specificity of two indices.ResultsThe values obtained for the sensitivity to false‐positive ratio for CPI ranged widely from 1.12 to 7.71, indicating regional variation in both errors. The calibrated adjusted odds ratio for smoking vs non‐smoking was higher than the uncalibrated odds ratio for PD defined by CPI (2.75 (2.01, 3.77) vs 2.02 (1.63, 2.52)) and LA (3.85 (2.44, 6.13) vs 1.93 (1.47, 2.54)) scores. Similar underestimation was noted for other risk factors.ConclusionThe effects of risk factors on PD measured using CPI and LA in a large population‐based survey were underestimated without correcting for measurement errors.

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Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model

Sakai¹,

Kobayashi²,

Nakamura³

et al. 2007

Methods Inf Med

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Summary Objectives : The diagnosis of acute appendicitis is difficult, and a diagnostic error will often lead to either a perforation or the removal of a normal appendix. In this study, we constructed a Bayesian network model for the diagnosis of acute appendicitis and compared the diagnostic accuracy with other diagnostic models, such as the naive Bayes model, an artificial neural network model, and a logistic regression model. Methods : The data from 169 patients, who suffered from acute abdominal pain and who were suspected of having an acute appendicitis, were analyzed in this study. Nine variables were used for the evaluation of the accuracy of the four models for the diagnosis of an acute appendicitis. The naive Bayes model, the Bayesian network model, an artificial neural network model, and a logistic regression model were used i this study for the diagnosis of acute appendicitis. These four models were validated by using the “632 + bootstrap method” for resampling. The levels of accuracy of the four models for diagnosis were compared by the error rates and by the areas under the receiver operating characteristic curves. Results : Through the course of illness, 50.9% (86 of 169) of the patients were diagnosed as having an acute appendicitis. The error rate was the lowest in the Bayesian network model, as compared with the other diagnostic models. The area under the receiver operating characteristic curve analysis also showed that the Bayesian network model provided the most reliable results. Conclusion : The Bayesian network model provided the most accurate results in comparison to other models for the diagnosis of acute appendicitis.

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Bayesian Random-effect Model for Predicting Outcome Fraught with Heterogeneity

Abstract: The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability.

Cited by 3 publications

References 15 publications

Analysis of household data on influenza epidemic with Bayesian hierarchical model

Analysis of household data on influenza epidemic with Bayesian hierarchical model

Effects of risk factors on periodontal disease defined by calibrated community periodontal index and loss of attachment scores

Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model

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