scholarly journals Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection

2017 ◽  
Vol 28 (4) ◽  
pp. 1126-1140 ◽  
Author(s):  
Rianne Jacobs ◽  
Emmanuel Lesaffre ◽  
Peter FM Teunis ◽  
Michael Höhle ◽  
Jan van de Kassteele
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Missing Values ◽  
Disease Outbreaks ◽  
Food Products ◽  
Bayesian Variable Selection ◽  
Small Sample ◽  
Case Control Studies ◽  
Food Borne ◽  
Contaminated Food

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues.

scholarly journals Bayesian Variable Selection Methods for Matched Case-Control Studies

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Josephine Asafu-Adjei ◽  
Mahlet G. Tadesse ◽  
Brent Coull ◽  
Raji Balasubramanian ◽  
Michael Lev ◽  
...  
Keyword(s):  
Variable Selection ◽  
Statistical Power ◽  
High Efficiency ◽  
Massachusetts General Hospital ◽  
Imaging Study ◽  
Bayesian Variable Selection ◽  
Case Control ◽  
Selection Methods ◽  
Case Control Studies ◽  
Matched Case

AbstractMatched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative.

Bayesian Variable Selection for Semiparametric Logistic Regression

2021 ◽  
Vol 26 (5) ◽  
pp. 44-57
Author(s):  
Zainab Sami ◽  
Taha Alshaybawee
Keyword(s):  
Logistic Regression ◽  
Variable Selection ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Numerical Data ◽  
Real Data ◽  
Bayesian Variable Selection ◽  
Bayesian Lasso ◽  
Posterior Inference ◽  
Semiparametric Logistic Regression

Lasso variable selection is an attractive approach to improve the prediction accuracy. Bayesian lasso approach is suggested to estimate and select the important variables for single index logistic regression model. Laplace distribution is set as prior to the coefficients vector and prior to the unknown link function (Gaussian process). A hierarchical Bayesian lasso semiparametric logistic regression model is constructed and MCMC algorithm is adopted for posterior inference. To evaluate the performance of the proposed method BSLLR is through comparing it to three existing methods BLR, BPR and BBQR. Simulation examples and numerical data are to be considered. The results indicate that the proposed method get the smallest bias, SD, MSE and MAE in simulation and real data. The proposed method BSLLR performs better than other methods. 

scholarly journals Yersinia Phages and Food Safety

Viruses ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1105 ◽  
Author(s):  
Carlos G. Leon-Velarde ◽  
Jin Woo Jun ◽  
Mikael Skurnik
Keyword(s):  
Food Safety ◽  
Erythema Nodosum ◽  
Slow Growth ◽  
Food Products ◽  
Diagnostic Tools ◽  
Common Source ◽  
Slow Growth Rate ◽  
Food Borne ◽  
Enteric Infections ◽  
Contaminated Food

One of the human- and animal-pathogenic species in genus Yersinia is Yersinia enterocolitica, a food-borne zoonotic pathogen that causes enteric infections, mesenteric lymphadenitis, and sometimes sequelae such as reactive arthritis and erythema nodosum. Y. enterocolitica is able to proliferate at 4 °C, making it dangerous if contaminated food products are stored under refrigeration. The most common source of Y. enterocolitica is raw pork meat. Microbiological detection of the bacteria from food products is hampered by its slow growth rate as other bacteria overgrow it. Bacteriophages can be exploited in several ways to increase food safety with regards to contamination by Y. enterocolitica. For example, Yersinia phages could be useful in keeping the contamination of food products under control, or, alternatively, the specificity of the phages could be exploited in developing rapid and sensitive diagnostic tools for the identification of the bacteria in food products. In this review, we will discuss the present state of the research on these topics.

Bayesian variable selection in logistic regression with application to whole-brain functional connectivity analysis for Parkinson’s disease

2020 ◽  
pp. 096228022097899
Author(s):  
Xuan Cao ◽  
Kyoungjae Lee ◽  
Qingling Huang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Logistic Regression ◽  
Functional Connectivity ◽  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Whole Brain ◽  
Model Selection Consistency ◽  
Brain Functional Connectivity ◽  
T Distribution

Parkinson’s disease is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this paper, we propose a Bayesian model to predict Parkinson’s disease employing a functional MRI (fMRI) based radiomics approach. We consider a spike and slab prior for variable selection in high-dimensional logistic regression models, and present an approximate Gibbs sampler by replacing a logistic distribution with a t-distribution. Under mild conditions, we establish model selection consistency of the induced posterior and illustrate the performance of the proposed method outperforms existing state-of-the-art methods through simulation studies. In fMRI analysis, 6216 whole-brain functional connectivity features are extracted for 50 healthy controls along with 70 Parkinson’s disease patients. We apply our method to the resulting dataset and further show its benefits with a higher average prediction accuracy of 0.83 compared to other contenders based on 10 random splits. The model fitting procedure also reveals the most discriminative brain regions for Parkinson’s disease. These findings demonstrate that the proposed Bayesian variable selection method has the potential to support radiological diagnosis for patients with Parkinson’s disease.

scholarly journals Bayesian variable selection based on clinical relevance weights in small sample studies—Application to colon cancer

2019 ◽  
Vol 38 (12) ◽  
pp. 2228-2247 ◽  
Author(s):  
Sandrine Boulet ◽  
Moreno Ursino ◽  
Peter Thall ◽  
Anne‐Sophie Jannot ◽  
Sarah Zohar
Keyword(s):  
Colon Cancer ◽  
Variable Selection ◽  
Clinical Relevance ◽  
Bayesian Variable Selection ◽  
Small Sample
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