Augmented mixed beta regression models for periodontal proportion data

Continuous (clustered) proportion data often arise in various domains of medicine and public health where the response variable of interest is a proportion (or percentage) quantifying disease status for the cluster units, ranging between zero and one. However, because of the presence of relatively disease‐free as well as heavily diseased subjects in any study, the proportion values can lie in the interval [0,1]. While beta regression can be adapted to assess covariate effects in these situations, its versatility is often challenged because of the presence/excess of zeros and ones because the beta support lies in the interval (0,1). To circumvent this, we augment the probabilities of zero and one with the beta density, controlling for the clustering effect. Our approach is Bayesian with the ability to borrow information across various stages of the complex model hierarchy and produces a computationally convenient framework amenable to available freeware. The marginal likelihood is tractable and can be used to develop Bayesian case‐deletion influence diagnostics based on q‐divergence measures. Both simulation studies and application to a real dataset from a clinical periodontology study quantify the gain in model fit and parameter estimation over other ad hoc alternatives and provide quantitative insight into assessing the true covariate effects on the proportion responses. Copyright © 2014 John Wiley & Sons, Ltd.     

In the Search of Marine Pestiviruses: First Case of Phocoena Pestivirus in a Belt Sea Harbour Porpoise

Pestiviruses are widespread pathogens causing severe acute and chronic diseases among terrestrial mammals. Recently, Phocoena pestivirus (PhoPeV) was described in harbour porpoises (Phocoena phocoena) of the North Sea, expanding the host range to marine mammals. While the role of the virus is unknown, intrauterine infections with the most closely related pestiviruses— Bungowannah pestivirus (BuPV) and Linda virus (LindaV)—can cause increased rates of abortions and deaths in young piglets. Such diseases could severely impact already vulnerable harbour porpoise populations. Here, we investigated the presence of PhoPeV in 77 harbour porpoises, 277 harbour seals (Phoca vitulina), grey seals (Halichoerus grypus) and ringed seals (Pusa hispida) collected in the Baltic Sea region between 2002 and 2019. The full genome sequence of a pestivirus was obtained from a juvenile female porpoise collected along the coast of Zealand in Denmark in 2011. The comparative Bayesian phylogenetic analyses revealed a close relationship between the new PhoPeV sequence and previously published North Sea sequences with a recent divergence from genotype 1 sequences between 2005 and 2009. Our findings provide further insight into the circulation of PhoPeV and expand the distribution from the North Sea to the Baltic Sea region with possible implications for the vulnerable Belt Sea and endangered Baltic Proper harbour porpoise populations.     

EczemaPred: A computational framework for personalised prediction of eczema severity dynamics

BackgroundAtopic dermatitis (AD) is a chronic inflammatory skin disease leading to substantial quality of life impairment with heterogeneous treatment responses. People with AD would benefit from personalised treatment strategies, whose design requires predicting how AD severity evolves for each individual.ObjectiveThis study aims to develop a computational framework for personalised prediction of AD severity dynamics.MethodsWe introduced EczemaPred, a computational framework to predict patient‐dependent dynamic evolution of AD severity using Bayesian state‐space models that describe latent dynamics of AD severity items and how they are measured. We used EczemaPred to predict the dynamic evolution of validated patient‐oriented scoring atopic dermatitis (PO‐SCORAD) by combining predictions from the models for the nine severity items of PO‐SCORAD (six intensity signs, extent of eczema, and two subjective symptoms). We validated this approach using longitudinal data from two independent studies: a published clinical study in which PO‐SCORAD was measured twice weekly for 347 AD patients over 17 weeks, and another one in which PO‐SCORAD was recorded daily by 16 AD patients for 12 weeks.ResultsEczemaPred achieved good performance for personalised predictions of PO‐SCORAD and its severity items daily to weekly. EczemaPred outperformed standard time‐series forecasting models such as a mixed effect autoregressive model. The uncertainty in predicting PO‐SCORAD was mainly attributed to that in predicting intensity signs (75% of the overall uncertainty).ConclusionsEczemaPred serves as a computational framework to make a personalised prediction of AD severity dynamics relevant to clinical practice. EczemaPred is available as an R package.     

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study

BackgroundIdentifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction.ObjectiveThe aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk.MethodsLarge-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using the R package bnlearn.ResultsAge, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95% CI 0.865-0.867), sensitivity of 0.752 (95% CI 0.749-0.756), specificity of 0.882 (95% CI 0.879-0.885), and accuracy of 0.882 (95% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation.ConclusionsThe findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision-making by providing an estimated risk of GBS for a specific vaccinee or be developed into an open-access platform for vaccinees’ self-monitoring.     

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees

ObjectiveAfter deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees. Materials and MethodsWe introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive “Type I and II” regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic reviser in terms of the average loss.ResultsBoth procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818–0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230–0.300) and 0.241 (95%CI, 0.216–0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765–0.769) to 0.800 (95%CI, 0.798–0.802) and 0.799 (95%CI, 0.797–0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913–0.935) and 0.925 (95%CI, 0.914–0.935), compared to the static model’s aAUC of 0.904 (95%CI, 0.892–0.916).DiscussionDespite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data.ConclusionsBLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time.     

Identification of Multiple Mechanical Properties of Laminates from a Single Compressive Test

In-plane elastic and interlaminar properties of composite laminates are commonly obtained through separate experiments. In this paper, a simultaneous identification method for both properties using a single experiment is proposed. The mechanical properties of laminates were treated as uncertainties and Bayesian inference was employed with measured strain-load curves in compression tests of laminates with embedded delamination. The strain–load curves were separated into two stages: the pre-delamination stage and the post-delamination stage. Sensitivity analysis was carried out to determine the critical properties at different stages, in order to alleviate the ill-posed problem in inference. Results showed that the in-plane Young’s modulus and shear modulus in elastic properties are dominant in the pre-delamination stage, and the interlaminar strength and type I fracture toughness in interlaminar properties are dominant in the post-delamination stage. Five times of property identification were carried out; the maximum coefficient of variation of identified properties was less than 1.11%, and the maximum error between the mean values of the identified properties and the ones from standard experiments was less than 5.44%. The proposed method can reduce time and cost in obtaining multiple mechanical properties of laminates.     

Probabilistic Assessment of the Dynamic Viscosity of Self-Compacting Steel-Fiber Reinforced Concrete through a Micromechanical Model

This article develops a probabilistic approach to a micromechanical model to calculate the dynamic viscosity in self-compacting steel-fiber reinforced concrete (SCSFRC), which implies a paradigm shift in the approach of the deterministic models used. It builds on a previous work by the authors in which Bayesian analysis is applied to rheological micromechanical models in cement paste, self-compacting mortar, and self-compacting concrete. As a consequence of the varied characteristics of the particles in these suspensions (in terms of materials, shapes, size distributions, etc.), as well as their random nature, it seems appropriate to study these systems with probabilistic models. The Bayesian analysis, thorough Markov Chain Monte Carlo and Gibbs Sampling methods, allows the conversion of parametric-deterministic models into parametric-probabilistic models, which results in enrichment in engineering and science. The incorporation of steel fibers requires a new term in the model to account for their effect on the dynamic viscosity of SCSFRC, and this new term is also treated here with the Bayesian approach. The paper uses an extensive collection of experimental data to obtain the probability density functions of the parameters for assessing the dynamic viscosity in SCSFRC. The results obtained with these parameters’ distributions are much better than those calculated with the theoretical values of the parameters, which indicates that Bayesian methods are appropriated to respond to questions in complex systems with complex models.     

Gross energy metabolism in mice under late onset, short term caloric restriction

Late onset, short-term moderate caloric restriction (CR) may have beneficial health effects. A 26% CR regime induced at 14 months of age for 70 days in male C57Bl/6 (ICRFa) mice resulted in a reduction in body mass of 17%. A decrease in daily energy expenditure was associated with decreased body mass in CR mice. There was no difference in total levels of physical activity between the CR and ad libitum (AL) groups; however, activity patterns were different. We developed a Bayesian model to dissect the impact of food anticipation activity (FAA) and feeding on physical activity. FAA was stronger in CR mice and remaining basal activity was higher in AL mice, but CR mice displayed larger diurnal variations as well as a phase shift in their diurnal activity. CR mice displayed lower body temperature, especially late during the dark phase. This was due to lower basal (activity-independent) temperature at all times of the day, coupled to a phase shift in the diurnal rhythm. The correlation between body temperature and physical activity was independent of feeding regimen and light/dark cycles. Reduction of body mass and basal temperature were major compensatory mechanisms to reduced food availability during late-onset, short-term CR.     

Variables linked to hepatitis B vaccination success in non-dialyzed chronic kidney disease patients: Use of a bayesian model

BackgroundHepatitis B vaccination is recommended for chronic kidney disease (CKD) patients before starting dialysis. We performed an analyis aimed to describe the clinical and biological parameters related to the success of vaccination in CKD patients before starting dialysis.MethodsWe extracted data of 170 non-dialyzed patients who were offered hepatitis B vaccination from a register. They received a first vaccination of 40 μg followed by boosters after one, two and six months. Patients were considered protected if their hepatitis B antibody level was > 10 IU/L, three months apart. A logistic regression and a Bayesian model were used to describe the relationships between variables and the success of vaccination.ResultsVaccination protected 50.6% of the patients. Model adjustment to the data was higher using the Bayesian model compared to the logistic regression (with area under the ROC curve of 0.955 ± 0.007 vs 0.775 ± 0.066 respectively). The Bayesian model's robustness studied using a 10 fold cross validation showed a percentage of misclassified subjects of 12.4 ± 1.8%, a sensitivity of 87.7 ± 0.3%, a specificity of 87.5 ± 0.3%, a positive predictive value of 87.8 ± 0.3% and negative predictive value of 87.4 ± 0.2%. As classified by the Bayesian model, the variables most related to successful vaccination were, in descending order: age, eGFR, protidemia, albuminemia, cause of renal failure, gender, previous vaccination and weight.ConclusionThe Bayesian network confirmed that both kidney function and nutritional status of patients are important factors to explain the success of vaccination against hepatitis B in CKD patients before dialysis. For research purposes, before an external validation, the network can be used online at www.hed.cc/?s=Bhepatitis&n=ReseauhepatiteBsup10.neta.