Posterior Distribution Research Articles

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure.

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Choroidal Nevi in Children: Size, Growth, and Topographic Distribution.

To explore size, growth, and topographic distribution of choroidal nevi in children to gain insights into choroidal nevogenesis. Retrospective consecutive case series using pediatric clinic - and population - study data, comparing to adult data. Clinical data from Cole Eye Institute (CEI) database (December 2005-January 2023) was derived from a retrospective consecutive case series of 20 children (< 20 years) with choroidal nevi. For population data, 48 children from previously reported pooled data of the participants of Sydney Pediatric Eye Disease Study, Sydney Myopia Study, Sydney Childhood Eye Study, and Sydney Adolescent Vascular and Eye Disease Study were included. Fundus photographs were reviewed and the locations of 18 choroidal nevi seen at CEI with widefield imaging were mapped on a radial scatter plot. For comparison, 100 consecutive adults with choroidal nevi were identified from CEI database. Main outcomes were size, growth, and topographic distribution of choroidal nevi. The median largest basal diameter was 1.6 mm (range 0.4-4.2) in children. Most choroidal nevi (75%) remained stable, and 16% demonstrated growth at follow-up. The mean growth rate was calculated as 0.12 mm/year (range 0.10-0.15). Malignant transformation was not noted during childhood. All secondary changes (drusen, orange pigment, and subretinal fluid) associated with choroidal nevi in children were less common than those in adults (p<0.05). Choroidal nevi in children were located significantly more posterior than in adults. The median distance to fovea was 2.1 mm (range 0.5-8.5) in children and 5.1 mm (range 0.4-16) in adults (p<0.0001). The onset and growth of choroidal nevi in children suggest active choroidal nevogenesis in childhood. A posterior topographic distribution may support the developmental framework for migration and maturation of choroidal melanoblasts.

Bayesian risk prediction model: An accessible strategy to predict cadmium contamination risk in wheat grain grown in alkaline soils.

Excessive cadmium (Cd) concentration in wheat grain is becoming a widespread concern in China. Considering the complexity of Cd transfer in the soil-wheat system, how the Cd risk in wheat grain be accurately predicted from the limited details available is of great significance for the risk management of Cd. Bayes' theory could leverage existing data by combining prior information and observational data, providing a promising strategy with which to calculate a more robust posterior probability of a grain sample exceeding the food safety standard (FSS) for Cd (0.1mgkg-1). In the current study, a risk prediction model, based on Bayes' theory, was established to achieve a more accurate prediction of the wheat grain Cd risk from a limited number of soil parameters. The risk prediction model could predict the risk probability of wheat grain with a Cd concentration exceeding the FSS under a given soil concentration of either total Cd or diethylenetriaminepentaacetic acid (DTPA)-extractable Cd. Soil total Cd concentration proved to be a better variable for the model with greater predictive accuracy. The model predicted that fewer than 5% of the wheat grain would have a Cd concentration exceeding the FSS when grown in soil with a total Cd concentration of less than 0.299mgkg-1. The risk probability rose significantly to 50% when the soil total Cd reached 0.778mgkg-1. The accuracy of the model was greater than the widely applied multiple linear regression model, whereas previously published data from similar soil conditions also confirmed that the Bayesian model could predict wheat Cd risk with minimal error. The proposed model provides an accurate, accessible and cost-effective methodology for predicting Cd risk in wheat grown in alkaline soils before harvest. The wider application to other soil conditions, crops or contaminants using the Bayesian model is also promising for risk management authorities.

A comparison of computational algorithms for the Bayesian analysis of clinical trials.

Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea

Summary In this article, we use a new workflow to substantiate the characterization of a prominent, deep sediment conductor in the hyper-extended Bjørnøya Basin (SW Barents Sea) previously identified in smooth resistivity models from 3D deterministic inversion of magnetotelluric data. In low dimensionality environments like layered sedimentary basin, 1D Bayesian inversion can be advantageous for a thorough exploration of the solution space but the violation of the 1D assumption has to be efficiently handled. The primary geological objectives of this work is therefore preceded by a secondary task: the application of a new machine learning approach for handling the 1D violation assumption for 21 MT field stations in the Barents Sea. We find that a decision tree can adequately learn the relationship between MT dimensionality parameters and the 1D-3D residual response for a training set of synthetic models, mimicking typical resistivity structures of the SW Barents Sea. The machine learning model is then used to predict the dimensionality compensation error for MT signal periods ranging of 1 to 3000 s for 21 receivers located over the Bjørnøya Basin and Veslemøy High. After running 1D Bayesian inversion, we generated a posterior resistivity distribution for an ensemble of 6000 1D models fitting the compensated MT data for each 21 field stations. The proportion of 1D models showing ρ &amp;lt; 1 Ω.m is consistently beyond 80% and systemically reaches a maximum of 100% in the Early Aptian - Albian interval in the Bjørnøya Basin. In hyper-extended basins of the SW Barents Sea, the dimensionality compensation workflow has permitted to refine the characterization of the deep basin conductor by leveraging the increased vertical resolution and optimal used of MT data. In comparison, the smooth 3D deterministic models only poorly constrained depth and lateral extent of the basin anomaly. The highest probability of finding ρ &amp;lt; 1 Ω.m is robustly assigned to the syn-tectonic Early Aptian - Albian marine shales, now buried at 6 to 8 km depth. Based on a theoretical two phase fluid-rock model, we show that the pore fluid of these marine shales must have a higher salinity than seawater to explain the anomaly ρ &amp;lt; 1 Ω.m. Therefore, the primary pore fluid underwent mixing with a secondary brine during rifting. Using analogue rift systems in paleomargins, we argue that two possible secondary brine reservoir may contribute to deep saline fluid circulation in the hyper-extended basin: (1) Permian salt-derived fluid and, (2) mantle-reacted fluid from serpentinization.

An improved Bayesian filter for nonlinear systems under multistep randomly delayed and lost measurements

AbstractThis article addresses the Bayesian filtering problem for a class of nonlinear systems under multistep randomly delayed and lost measurements. A new measurement model is established that can characterize the random delay and loss of measurement data. First, an augmented Gaussian mixture filter framework is developed in the case of random delay of measurement data; the posterior probability density function after state augmentation is calculated by marginalizing over delay variables to extract accurate information from delayed measurements. The implementation of the filter is transformed into the computation of nonlinear numerical integrals. Second, under the proposed framework, novel expressions of the mean and covariance are generated by propagating the measurement taken at the previous moment in the event of no new measurement being received. Finally, we present two simulation examples for estimating system states, and the results demonstrate the effectiveness and superiority of our proposed filter.

A Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation.

ABSTRACT An alternative transcription start site (ATSS) is a major driving force for increasing the complexity of transcripts in human tissues. As a transcriptional regulatory mechanism, ATSS has biological significance. Many studies have confirmed that ATSS plays an important role in diseases and cell development and differentiation. However, exploration of its dynamic mechanisms remains insufficient. Identifying ATSS change points during cell differentiation is critical for elucidating potential dynamic mechanisms. For relative ATSS usage as percentage data, the existing methods lack sensitivity to detect the change point for ATSS longitudinal data. In addition, some methods have strict requirements for data distribution and cannot be applied to deal with this problem. In this study, the Bayesian change point detection model was first constructed using reparameterization techniques for two parameters of a beta distribution for the percentage data type, and the posterior distributions of parameters and change points were obtained using Markov Chain Monte Carlo (MCMC) sampling. With comprehensive simulation studies, the performance of the Bayesian change point detection model is found to be consistently powerful and robust across most scenarios with different sample sizes and beta distributions. Second, differential ATSS events in the real data, whose change points were identified using our method, were clustered according to their change points. Last, for each change point, pathway and transcription factor motif analyses were performed on its differential ATSS events. The results of our analyses demonstrated the effectiveness of the Bayesian change point detection model and provided biological insights into cell differentiation.

Epidemiology of acute lymphoblastic leukaemia in Sardinia, Italy: Age, sex, and environmental correlates

Using a database of 1974–2003 incident cases of haematological malignancies, we explored the time trend, geographic spread and socio-economic and environmental correlates of ALL incidence in Sardinia, Italy, by sex and age. The age- and sex-standardized (World population) ALL incidence rate was 2.0 per 100,000 (95% CI 1.8 – 2.1) and showed variable trend patterns by sex and age. In the total population, ALL incidence showed an annual per cent change of −1.4% (95% CI -0.59 – -3.34) over the study period, with a knot separating a downward slope in 1974–1996 from an increase in 1996–2003. ALL incidence replicated such pattern in women but not men, whose incidence did not substantially vary over the study period (APC = −2.57%, 95% CI -5.45 – 0.26). Among women, the spatial analysis suggested a clustering of ALL in the southwestern part of the region, whilst only a commune had a high posterior probability of a high ALL incidence among men. Three unrelated communes showed a high posterior probability of ALL at age ≤ 24; only the most populated urban centre showed excess cases at age ≥ 25 years. There was no correlation between the geographic spread of ALL at ages ≤ 24 and ≥ 25 years (p = 0.082). Urban residence was a risk factor for the younger age group. Residences near industrial settlements and in the most populated urban centre were risk factors for subjects aged ≥ 25 years. Our findings suggest age-related differences in ALL aetiology.

Bayesian Inference for Multiple Datasets

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here, we use the AMIS approach to fit a von Mises distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from 3D meteorological data. A posterior distribution of parameters can help to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and the pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. The novelty of our study is in testing the performance of the method on a very large number of datasets (&gt;11,000). Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Genomic epidemiology of Staphylococcus aureus from the Iberian Peninsula highlights the expansion of livestock associated-CC398 towards wildlife

Staphylococcus aureus is a versatile pathobiont, exhibiting a broad host range, including humans, other mammals, and avian species. Host specificity determinants, virulence, and antimicrobial resistance genes are often shared by strains circulating at the animal-human interface. While transmission dynamics studies have shown strain exchange between humans and livestock, knowledge of the source, genetic diversification, and transmission drivers of S. aureus in wildlife lag behind.In this work, we explore a wide array of S. aureus genomes from different sources in the Iberian Peninsula to understand population structure, gene content and niche adaptation at the human-livestock-wildlife nexus. Through Bayesian inference, we address the hypothesis that S. aureus strains in wildlife originate from humanized landscapes, either from contact with humans or through interactions with livestock. Phylogenetic reconstruction applied to whole genome sequence data was completed with a dataset of 450 isolates featuring multiple clones from the 1990–2022 period and a subset of CC398 strains representing the 2008–2022 period. Phylodynamic signatures of S. aureus from the Iberian Peninsula suggest widespread circulation of most clones among humans before jumping to other hosts. The number of transitions of CC398 strains within each host category (human, livestock, wildlife) was high (88.26 %), while the posterior probability of transitions from livestock to wildlife was remarkably high (0.99). Microbial genome-wide association analysis did not evidence genome rearrangements nor biomarkers suggesting S. aureus niche adaptation to wildlife, thus supporting recent spill overs. Altogether, our findings indicate that S. aureus isolates collected in the past years from wildlife most likely represent multiple introduction events from livestock. The clonal origin of CC398 and its potential to disseminate and evolve through different animal host species are highlighted, calling for management practices at the livestock-wildlife axis to improve biosecurity and thus restrict S. aureus transmission and niche expansion along gradients of human influence.

Classification performance of the Sella-Tunis et al. (2017) sex estimation method in Czech population: different posterior probability threshold approaches.

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

Task-Dependent Brain Activity in Generalized Anxiety Disorder Determined By Bayesian Spatio-Temporal Single Case Model

Introduction: Data obtained from functional magnetic resonance imaging (fMRI) have a complex structure. Considering the special features of this type of data in analyses is of particular importance. Previous studies on generalized anxiety disorder (GAD) as a prevalent mental disorder using functional neuroimaging have had conflicting results. In this study, we apply a Bayesian spatiotemporal model to this type of data which considers both spatial and temporal dependence among regions which is one of the most essential features to consider. Methods: In this single-subject study, we analyze data from a patient with GAD and a healthy participant. Both participants are 24-year-old women who are assigned an emotion reactivity task (matching neutral and negative facial expressions) inside a scanner. The spatial Bayesian variable selection method is used to detect blood oxygen level-dependent activation in fMRI data. Results: Activation areas in neutral and negative facial expressions are provided for both participants by posterior probability map. The results of our study show a greater level of activity in the GAD participant in comparison to the healthy participant in responding to the negative matching task. Conclusion: the GAD patient showed more neural activity in response to negative facial expressions than the healthy participant in brain regions related to emotional response in the areas of the frontal Pole, middle frontal gyrus, insular cortex, and frontal orbital cortex. Moreover, the inferior frontal gyrus in the patient with GAD showed more reaction to negative emotional stimuli.

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

First report of leaf spot on Daphniphyllum macropodum caused by Neopestalotiopsis clavispora in China.

Daphniphyllum macropodum Miq., an evergreen arbor, is widely cultivated in southern China for its ornamental and medicinal value (Su et al. 2013). In October 2019, a severe leaf spot was observed on D. macropodum in Jinggangshan National Nature Reserve in Ji'an city, Jiangxi, China (114°06'23″E, 26°32'25″N). The plants were about 15 years old, and the disease incidence was estimated to be 15% (4/26 plants). The disease primarily appeared as small black spots on the leaves. At the late stage, the spots enlarged and coalesced into regular or irregular gray necrotic lesions with dark margins. We collected five samples per plant and total 20 samples were collected to isolated the pathogen strains. The margin of the diseased tissues was cut into 5 mm × 5 mm pieces; surface disinfected with 70% ethanol and 2% NaOCl for 30 s and 60 s, respectively; and rinsed thrice with sterile water. Tissues were placed on potato dextrose agar (PDA) and incubated at 25°C in the dark. Pure cultures were obtained by single-spore isolation method, and the representative isolates, JRM3, JRM6, and JRM8 were used for morphological studies and phylogenetic analyses. The colonies of three isolates grown on PDA were white, cottony, and flocculent, contained undulate edges with dense aerial mycelium on the surface at 25 °C. Conidiomata was black conidial masses on PDA. Conidia were 5-celled, clavate to fusiform, smooth, 19.3 to 24.4 long × 6.1 to 8.6 μm wide (n = 50). The 3 median cells were dark brown to olivaceous, the central cell was darker than the other 2 cells, and the basal and apical cells were hyaline. All conidia developed one basal appendage (3.4 to 8.3 μm long; n = 50), and 2 to 3 apical appendages (18 to 32 μm long; n = 50), filiform. The morphological characteristics of the isolates are comparable with those of the genus Neopestalotiopsis (Maharachchikumbura et al. 2014). The internal transcribed spacer (ITS) regions, β-tubulin 2 (TUB2) and translation elongation factor 1-alpha (TEF1-α) were amplified from genomic DNA for the three isolates using primers ITS1/ITS4, T1/Bt-2b, EF1-728F/EF-2 (Maharachchikumbura et al. 2014), respectively. The sequences of the isolates were submitted to GenBank (ITS, OQ372202 to OQ372204; TUB2, OQ390129 to OQ390131; TEF1-α, OQ390126 to OQ390128). A maximum likelihood and Bayesian posterior probability analyses using IQtree v. 1.6.8 and Mr. Bayes v. 3.2.6 with the concatenated sequences placed JRM3, JRM6, and JRM8 in the clade of N. clavispora. Based on the multi-locus phylogeny and morphology, three isolates were identified as N. clavispora. To confirm pathogenicity, eight healthy 10-year-old D. macropodum plants growing in the field were chosen, and 4 leaves per plant were wounded with a sterile needle and inoculated with 10 μL conidial suspension per leaf (106 conidia/ml). Eight plants inoculated with sterile water were used as control. All the inoculated leaves were covered with plastic bags to maintian a humidity environment for 2 days. The leaves inoculated with conidial suspension showed similar symptoms to those observed in the field, whereas control leaves were asymptomatic for 10 days. The same fungus were re-isolated from the lesions, whereas no fungus was isolated from control leaves. N. clavispora was determined as the pathogen of a variety of plant diseases, including Kadsura coccinea (Xie et al. 2018), Dendrobium officinale (Cao et al. 2022), Macadamia integrifolia (Santos et al. 2019). However, this is the first report of N. clavispora infecting D. macropodum in China. This work provided crucial information for epidemiologic studies and appropriate control strategies for this newly emerging disease.

A bi-endpoint expectation-maximisation algorithm for re-estimating sample size for the time-to-event endpoint under the blind condition

Abstract The expectation-maximisation (EM) algorithm can be used to adjust the sample size for the time-to-event endpoint without unblinding. Nevertheless, censoring or unreliable initial estimates may render inconsistent estimates by the EM algorithm. To address these limitations, we propose a bi-endpoint EM algorithm that incorporates the time-to-event endpoint and another endpoint, which can encompass various endpoint types and is not limited to efficacy indicators, during the EM iterations. Additionally, we suggest 2 approaches for choosing initial estimates. The application conditions are as follows: (i) at least one endpoint’s initial estimate is reliable and (ii) the influence of this endpoint on the posterior distribution of the latent variable exceeds that of another endpoint.

Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts

Uncertainty Quantification of Process- Property-Structure Linkage for Fused Filament Fabrication Parts

A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Clinical outcomes in patients with piperacillin/ tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae bloodstream infection.

A small proportion of Escherichia coli and Klebsiella pneumoniae demonstrate in vitro non-susceptibility to piperacillin/tazobactam but retain susceptibility to ceftriaxone. Uncertainty remains regarding how best to treat these isolates. We sought to compare clinical outcomes between patients with piperacillin/tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae bloodstream infection receiving definitive therapy with ceftriaxone versus an alternative effective antibiotic. We retrospectively identified patients with a positive blood culture for piperacillin/tazobactam-non-susceptible but ceftriaxone-susceptible E. coli or K. pneumoniae between 1 January 2013 and 31 December 2022. Patients were divided into one of two definitive treatment groups: ceftriaxone or alternative effective antibiotic. Our primary outcome was a composite of 90 day all-cause mortality, hospital readmission, or recurrence of infection. We used Cox proportional hazards models to compare time with the composite outcome between groups. Sixty-two patients were included in our analysis. Overall, median age was 63 years (IQR 49.5-71.0), the most common source of infection was intra-abdominal (25/62; 40.3%) and the median total duration of therapy was 12.0 days (IQR 9.0-16.8). A total of 9/22 (40.9%) patients in the ceftriaxone treatment group and 18/40 (45.0%) patients in the alternative effective antibiotic group met the composite endpoint. In an adjusted time-to-event analysis, there was no difference in the composite endpoint between groups (HR 0.67, 95% CI 0.30-1.50). The adjusted Bayesian posterior probability that the HR was less than or equal to 1 (i.e. ceftriaxone is as good or better than alternative therapy) was 85%. These findings suggest that ceftriaxone can be used to effectively treat bloodstream infections with E. coli or K. pneumoniae that are non-susceptible to piperacillin/tazobactam but susceptible to ceftriaxone.

A Bayesian Spatio-Temporal Modeling Approach to the Inverse Heat Conduction Problem

Abstract A wide range of science and engineering problems involve inferring the system behavior and physical models from indirect observations. These are known as inverse problems. Unlike forward problems (predictive models), inverse problems are difficult to solve due to ill-posedness arising from one or more of non-existence, non-uniqueness (existence of many solutions), or instability (small perturbations in the measurements emerging from noise could lead to large changes in the solution). Therefore, direct inversion of the inverse problem in complex real-world systems is impractical. Several modeling approaches have been developed and adopted in the literature. However, the existing approaches do not consider spatio-temporal relationships. Meanwhile, various real systems and processes exist whose inverse problems can be comfortably modeled using spatio-temporal modeling techniques. We present a spatio-temporal inverse model considering interaction effects for modeling this type of inverse problem. We adopted a Bayesian inference framework for the subsequent parameter estimation problem and developed a Gibbs sampling algorithm to sample from the posterior distribution of the parameter of interest. Finally, we evaluate the proposed methodology through case studies on a 1D heat simulation and pool boiling experiment, using multi-sensor thermocouple measurements to reconstruct heat flux. Compared to the conventional methods, the proposed method performs better and can be adopted for solving inverse heat conduction problems. Moreover, incorporating the spatio-temporal interaction term notably improved the model's performance.