Bayesian comparison of means of a mixed model with application to regression analysis.
Explore the source record for details and available documents.
Models, Theoretical↗
SEARCH · Search PubMed
Results for “Bayesian modelling”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Comparative Bayesian and traditional inference for gamma-modeled survival data.
Explore the source record for details and available documents.
Bayes Theorem↗
Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.
Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.
Bayes Theorem↗
ScITree: Scalable Bayesian inference of transmission tree from epidemiological and genomic data.
Phylodynamic models capture joint epidemiological-evolutionary dynamics during an outbreak, providing a powerful tool to enhance understanding and management of disease transmission. Existing phylodynamic approaches, however, mostly rely on various non-mechanistic or semi-mechanistic approximations of the underlying epidemiological-evolutionary process. Previous work by Lau and colleagues has shown that full Bayesian mechanistic models, without relying on these approximations, can enable highly accurate joint inference of the epidemiological-evolutionary dynamics including the unobserved transmission tree. However, the Lau method faces major computational bottlenecks. As the volume of genomic data collected during outbreaks continues to grow, it is crucial to develop scalable yet accurate phylodynamic methods. Here we propose a new Bayesian phylodynamic model, overcoming the major scalability issue in the previous method and enabling a readily deployable, yet accurate, phylodynamic modeling framework. Specifically, we develop a scalable spatio-temporal phylodynamic framework for inferring the transmission tree (ScITree) and other key epidemiological parameters considering the infinite sites assumption in modeling mutation on the sequence level, in contrast to the Lau method in which mutation was modeled explicitly on the nucleotide level. Our approach features full Bayesian implementation utilizing an exact likelihood to mechanistically integrate epidemiological and evolutionary processes. We develop a computationally-efficient data-augmentation Markov Chain Monte Carlo algorithm, inferring key model parameters and unobserved dynamics including the transmission tree. We assess performance of our method using multiple simulated outbreak datasets. Our results indicate that our method can achieve high inference accuracy, comparable to the performance of the Lau method. Additionally, our method scales significantly more efficiently for large outbreaks, with computing time increasing linearly with outbreak size, compared to the exponential scaling of the Lau method. We also demonstrate our method's utility by applying our validated modeling framework to a dataset describing a foot-and-mouth disease outbreak in the UK. Our results show that our method is able to generate estimates of the transmission dynamics consistent with those from the prior method, further demonstrating the robustness of our new approach. In summary, our method provides a computationally-efficient, highly scalable, accurate modeling framework for inferring the joint spatio-temporal dynamics of epidemiological and evolutionary processes, facilitating timely and effective outbreak responses in space and time. Our method is implemented in our R package ScITree.
Bayes Theorem↗
"Mental imbalance" and the prediction of recurrent delinquent behavior.
Cognitive and personality patterns of 84 court-referred adolescents were examined to identify predictors of recurrent delinquent behavior. Continued behavioral problems at follow-up were more likely in adolescents with discrepancies between Verbal and Performance IQ or large differences between "neurotic" and "psychotic" scale elevations on the MMPI. Positive outcomes were most likely for adolescents who could be described as "mildly neurotic." Combining the discrepancy scores from the intelligence and personality tests with other background variables in a Bayesian conditional probability model resulted in accurate predictions of later behavior for 81% of the sample. These findings suggest that imbalances in cognitive and personality development may limit a delinquent adolescent's ability to interact appropriately with the environment.
Adaptation, Psychological↗
31P NMR Bayesian spectral analysis of rat brain in vivo.
Bayesian spectrum analysis for parameter estimation is a rigorous statistical (non-Fourier-based) method. Herein the Bayesian quadrature NMR model is introduced and applied to analysis of 31P NMR time domain data from in vivo rat brain. Immunity to both the brain spectrum "baseline hump" and the phase twist is demonstrated.
Animals↗
Cell-type-specific genetic associations in Lewy body dementia identified using single-cell eQTL-based Mendelian randomization.
BACKGROUND: Lewy body dementia (LBD) is a complex neurodegenerative disorder marked by α-synuclein aggregation and dual impairment of cognitive and motor function.While genome-wide association studies have identified risk loci, the cellular mechanisms linking genetic variation to disease susceptibility remain largely unexplored. METHODS: We performed single-cell transcriptome-wide Mendelian randomization using brain cell-type-specific eQTLs across eight major cell types. Genetic associations were evaluated using inverse-variance weighted models, followed by Bayesian colocalization analysis. Replication was performed in independent stratified LBD cohorts based on APOE ε4 carrier status. Phenome-wide association analysis was included as a supplementary, descriptive assessment of cross-trait associations. RESULTS: Expression of ANKRD65 in excitatory neurons was significantly associated with reduced LBD risk (odds ratio = 0.65, 95 % CI: 0.52-0.81, p = 0.00013). This association passed a false discovery rate of 0.1 and showed strong evidence of colocalization (posterior probability = 0.93). Effect direction was consistent across APOE ε4+ and ε4- LBD subgroups in independent cohorts. No genome-wide significant associations were observed with non-neurological traits in the phenome-wide analysis. CONCLUSIONS: Our findings identify a genetically supported, cell-type-resolved association between ANKRD65 expression in excitatory neurons and LBD risk. This study demonstrates the value of integrating cell-resolved transcriptomic regulation with genetic inference to pinpoint functionally relevant targets in neurodegenerative diseases.
Humans↗
Phthalates and sex steroid hormones across the perimenopausal period: A longitudinal analysis of the Midlife Women's Health Study.
BACKGROUND: The menopausal transition involves significant sex hormone changes. Environmental chemicals, such as urinary phthalate metabolites, are associated with sex hormone levels in cross-sectional studies. Few studies have assessed longitudinal associations between urinary phthalate metabolite concentrations and sex hormone levels during menopausal transition. METHODS: Pre- and perimenopausal women from the Midlife Women's Health Study (MWHS) (n = 751) contributed data at up to 4 annual study visits. We quantified 9 individual urinary phthalate metabolites and 5 summary measures (e.g., phthalates in plastics (∑Plastic)), using pooled annual urine samples. We measured serum estradiol, testosterone, and progesterone collected at each study visit, unrelated to menstrual cycling. Linear mixed-effects models and hierarchical Bayesian kernel machine regression analyses evaluated adjusted associations between individual and phthalate mixtures with sex steroid hormones longitudinally. RESULTS: We observed associations between increased concentrations of certain phthalate metabolites and lower testosterone and higher sub-ovulatory progesterone levels, e.g., doubling of monoethyl phthalate (MEP), monobenzyl phthalate (MBzP), di-2-ethylhexyl phthalate (∑DEHP) metabolites, ∑Plastic, and ∑Phthalates concentrations were associated with lower testosterone (e.g., for ∑DEHP: -4.51%; 95% CI: -6.72%, -2.26%). For each doubling of MEP, certain DEHP metabolites, and summary measures, we observed higher mean sub-ovulatory progesterone (e.g., ∑AA (metabolites with anti-androgenic activity): 6.88%; 95% CI: 1.94%, 12.1%). Higher levels of the overall time-varying phthalate mixture were associated with lower estradiol and higher progesterone levels, especially for 2nd year exposures. CONCLUSIONS: Phthalates were longitudinally associated with sex hormone levels during the menopausal transition. Future research should assess such associations and potential health impacts during this understudied period.
Humans↗
The changing role of the exercise electrocardiogram as a diagnostic and prognostic test for chronic ischemic heart disease.
The exercise electrocardiogram has been the subject of intense research over the last 50 years, as both a diagnostic and prognostic method to assess patients with chronic ischemic heart disease. In 1986, the strengths and limitations of the technique to predict coronary and multivessel disease in clinical patient subsets are understood. The diagnostic accuracy of the test is improved by consideration of Bayesian theory, multivariate models and new non-ST segment criteria. Post-test coronary disease risk estimates are best reported in terms of a conditional probability, rather than statements of "positive" or "negative." The value of exercise testing in prognostic risk stratification is considerably enhanced by recent reports of long-term follow-up data in asymptomatic and symptomatic patients. Powerful prognostic information can be obtained when the clinical, electrocardiographic and physiologic data from the exercise test are used to formulate the post-test risk of a cardiac event, even in patients whose coronary anatomy is known. The changing role of the exercise electrocardiogram as a diagnostic and prognostic test is reviewed, with emphasis on the strengths and limitations of the procedure.
Angina Pectoris↗
BISON: bi-clustering of spatial omics data with feature selection.
MOTIVATION: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. RESULTS: To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. AVAILABILITY AND IMPLEMENTATION: The R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON.
Software↗
Investigating the zoonotic origins of ESBL-producing E. coli in community-acquired urinary tract infections in Ecuador.
Extended-spectrum β-lactamase-producing Escherichia coli (ESBL-producing E. coli) pose a growing global health threat. Although Latin America has been identified as a global hotspot of antimicrobial resistance, the zoonotic contribution to drug-resistant infections in the region remains poorly defined. We analyzed 137 clinical ESBL-producing E. coli isolates from urinary tract infections (UTIs) in Quito, Ecuador, applying a Bayesian latent class model informed by host-associated mobile genetic elements to estimate the fraction of infections attributable to food-animal sources. We estimated that 25.5% (35/137) of UTI isolates were putative zoonotic cases. This proportion rose to 42.5% after excluding ST131-H30, a human-associated pandemic lineage. Putative zoonotic isolates were enriched for animal-associated β-lactamase genes (e.g., blaTEM-1B, blaCTX-M-65), lacked human-associated markers such as blaOXA-1, and exhibited diverse antimicrobial resistance gene profiles resembling those observed among food-animal isolates. These isolates were also enriched for ColV-associated virulence genes typically linked to avian pathogenic E. coli. Putative zoonotic strains contributed substantially to third-generation cephalosporin-resistant UTIs in Quito, Ecuador, challenging assumptions derived from high-income settings that such infections are driven predominantly by human-to-human transmission. These findings highlight the importance of integrated One Health surveillance and mitigation, particularly in low- and middle-income countries where gaps in water, sanitation, and hygiene (WASH) may interact with antimicrobial use in food production to amplify antimicrobial resistance transmission.IMPORTANCEESBL-producing E. coli have rapidly emerged as a major global antimicrobial resistance threat. In Latin America, cephalosporins are commonly used in food-animal production, fueling the emergence of ESBL-producing E. coli. In low- and middle-income countries, excessive antimicrobial use driven by poorly regulated over-the-counter sales, combined with inadequate water, sanitation, and hygiene (WASH) infrastructure, can facilitate antimicrobial-resistant pathogen transmission from food animals to humans. Using a novel statistical-genomic approach, we found that over one in four cephalosporin-resistant UTIs in Quito, Ecuador, may be caused by E. coli strains originating from food animals. Our findings highlight the public health risks associated with antimicrobial use in food-animal production and the role of environmental and infrastructure-related vulnerabilities. As global demand for animal protein continues rising in middle-income countries, controlling zoonotic antimicrobial resistance transmission becomes increasingly urgent for protecting human health through integrated One Health strategies.
ESBL-producing E. coli↗
Bayesian classification of OXPHOS deficient skeletal myofibres.
Mitochondria are organelles in most human cells which release the energy required for cells to function. Oxidative phosphorylation (OXPHOS) is a key biochemical process within mitochondria required for energy production and requires a range of proteins and protein complexes. Mitochondria contain multiple copies of their own genome (mtDNA), which codes for some of the proteins and ribonucleic acids required for mitochondrial function and assembly. Pathology arises from genetic defects in mtDNA and can reduce cellular abundance of OXPHOS proteins, affecting mitochondrial function. Due to the continuous turn-over of mtDNA, pathology is random and neighbouring cells can possess different OXPHOS protein abundance. Estimating the proportion of cells where OXPHOS protein abundance is too low to maintain normal function is critical to understanding disease severity and predicting disease progression. Currently, one method to classify single cells as being OXPHOS deficient is prevalent in the literature. The method compares a patient's OXPHOS protein abundance to that of a small number of healthy control subjects. If the patient's cell displays an abundance which differs from the abundance of the controls then it is deemed deficient. However, due to the natural variation between subjects and the low number of control subjects typically available, this method is inflexible and often results in a large proportion of patient cells being misclassified. These misclassifications have significant consequences for the clinical interpretation of these data. We propose a single-cell classification method using a Bayesian hierarchical mixture model, which allows for inter-subject OXPHOS protein abundance variation. The model accurately classifies an example dataset of OXPHOS protein abundances in skeletal muscle fibres (myofibres). When comparing the proposed and existing model classifications to manual classifications performed by experts, the proposed model results in estimates of the proportion of deficient myofibres that are consistent with expert manual classifications.
Oxidative Phosphorylation↗
Optimal use of literature knowledge to improve the Bayesian diagnosis of coronary artery disease.
Bayes' theorem with the independence assumption is applied to a test sample of 141 subjects, using two sets of test sensitivities and specificities. The first set is derived by averaging over literature reports on the accuracy of the exercise electrocardiogram, exercise thallium scintigraphy, and carciac fluoroscopy. The second set of indices is derived by applying multivariate regression to the technical, population, and methodologic attributes obtained from the same literature by the use of meta-analysis. The meta-analytically corrected sensitivities and specificities resulted in significant improvement in the discriminatory power of the Bayes model. (Area under ROC curve increased, p = less than 0.01). However, the corrected model was not as accurate as a data-derived logistic regression model of the same test variables. Meta-analysis may be useful for modest improvement in the accuracy of literature-derived Bayesian models for predicting disease probabilities.
Adult↗
Impact of smoking on structural failure after arthroscopic rotator cuff repair: a systematic review and meta-analysis.
BACKGROUND: Rotator cuff tears cause significant shoulder pain and functional limitation. Arthroscopic rotator cuff repair improves symptoms, yet structural failure rates remain substantial. Smoking may impair tendon-to-bone healing, but clinical studies report mixed findings due to heterogeneous methodology. Therefore, a systematic synthesis of imaging-confirmed outcomes is needed to clarify the association between smoking and structural failure after arthroscopic rotator cuff repair. METHODS: This review followed PRISMA 2020 and was registered in PROSPERO (CRD420251246197). PubMed, Embase, Scopus, Web of Science, and the Cochrane Library were searched from inception to 12 December 2025. Comparative clinical studies of adults undergoing arthroscopic rotator cuff repair that reported imaging-confirmed structural integrity (magnetic resonance imaging or ultrasonography) at ≥6 months were included. Two reviewers independently screened studies, extracted data, and assessed quality using the Newcastle-Ottawa Scale. The primary outcome (structural failure) was pooled as risk ratios using a random-effects model with the restricted maximum likelihood estimator and Hartung-Knapp adjustment. Secondary continuous outcomes were synthesized using Bayesian random-effects models; subgroup, sensitivity, and meta-regression analyses explored heterogeneity. RESULTS: Ten cohort studies (1,683 shoulders) were included. Smoking was associated with a higher risk of imaging-confirmed structural failure (risk ratio 1.53; 95% confidence interval 1.13-2.08; P = .011) with low heterogeneity (I2 = 24.7%). Subgroup and sensitivity analyses supported robustness, with no evidence of effect modification by region, follow-up duration, tear size, or smoking definition. Meta-regression showed no significant influence of age, smoking prevalence, or diabetes prevalence on the pooled effect. Secondary outcomes (3 studies) suggested slightly lower postoperative American Shoulder and Elbow Surgeons scores among smokers, while visual analog scale pain scores and forward flexion showed no clear between-group differences. No publication-bias signals were detected for the primary outcome. CONCLUSION: Smoking is associated with a higher risk of imaging-confirmed structural failure after arthroscopic rotator cuff repair. Functional outcomes were broadly similar between groups, with only a small, likely clinically negligible reduction in American Shoulder and Elbow Surgeons scores among smokers. These findings support careful smoking history assessment and perioperative risk modification, including smoking cessation strategies.
Humans↗
Bayesian analysis of a three-component hierarchical design model.
Explore the source record for details and available documents.
Mathematics↗
Insights Into the Structural Features, Codon Usage Patterns, and Phylogenetic Analysis in Neoniphon argenteus (Teleostei: Holocentriformes) Based on Complete Mitochondrial Genome.
Neoniphon argenteus, a widely distributed nocturnal coral reef fish in the family Holocentridae, plays an important role in maintaining coral reef ecosystem health, yet its phylogenetic position remains poorly resolved. To bridge this gap, we sequenced and analyzed the complete mitochondrial genome of a specimen from the South China Sea to characterize its structural features, codon usage patterns, and phylogenetic relationships. The 16,569 bp mitogenome (GenBank: PP190474.1) encodes 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and two non-coding regions, exhibiting a distinct A + T bias. All tRNAs fold into typical cloverleaf secondary structures except tRNA-Ser (AGN), which lacks the dihydrouridine (DHU) arm. The control region contains palindromic motifs (TACAT/ATGTA) capable of forming hairpin structures and five conserved sequence blocks, whereas the OL region harbors a conserved 5'-GCCGG-3' motif. RSCU analysis revealed 31 frequently used codons (RSCU > 1) with a pronounced preference for A/C-ending codons. The ΔRSCU method identified 10 candidate optimal codons (GCA, CAA, GAA, GGA, AUU, CUA, CCA, CGA, ACA, and GUC). Selection pressure analysis using EasyCodeML and site-specific models indicated that all PCGs are predominantly under purifying selection, with no significant evidence of pervasive positive selection. ND6 exhibited elevated pairwise Ka/Ks ratios (mean = 1.209 ± 0.047), consistent with reduced selective constraint rather than adaptive evolution. Phylogenetic analysis of 19 Holocentriformes species using maximum likelihood and Bayesian inference with partitioned models based on 13 PCGs and two rRNA genes (12S and 16S) assigned all taxa to two well-supported subfamilies (Holocentrinae and Myripristinae). Within Holocentrinae, Neoniphon species form a monophyletic clade nested within a paraphyletic Sargocentron, suggesting that the genus Sargocentron as currently defined is not monophyletic. This study provides useful baseline molecular data for further exploration of the evolutionary history of N. argenteus and other members of Holocentriformes.
Holocentridae↗
Efficacy and safety of cannabinoid-based interventions for behavioral and cognitive symptoms in dementia: systematic review and meta-analysis.
BACKGROUND: Behavioral and cognitive symptoms are frequent in Alzheimer's disease and dementia, and available pharmacological options offer limited benefit. Cannabinoid-based therapies have been proposed as alternatives, but evidence remains inconclusive. METHODS: We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library through November 2025 for randomized controlled trials evaluating cannabinoids in Alzheimer's disease or dementia. Primary outcomes were agitation measured by the Cohen-Mansfield Agitation Inventory (CMAI) and neuropsychiatric symptoms assessed by the Neuropsychiatric Inventory-Nursing Home version (NPI-NH). Secondary outcomes included cognition using the Mini-Mental State Examination (MMSE) and adverse events. Standardized Mean Differences (SMDs) and Risk Ratios (RRs) were synthesized using random-effects (REML) and Bayesian random-effects models. Risk of bias was evaluated with RoB 2, and certainty of evidence with GRADE. RESULTS: Nine trials (334 participants) met inclusion criteria. Cannabinoids did not improve CMAI (SMD -0.58, 95% CI -1.71 to 0.55; I2 = 84%), NPI-NH total (SMD -0.02, 95% CI -1.00 to 0.96; I2 = 67%), NPI-NH agitation (SMD -0.44, 95% CI -1.45 to 0.57; I2 = 48%), or MMSE (SMD 0.86, 95% CI -16.33 to 18.06; I2 = 96%). Bayesian posterior estimates were close to zero, supporting the absence of effect. Leave-one-out analyses reduced heterogeneity only after excluding influential trials but did not alter results. Certainty of evidence was moderate for behavioral outcomes and low for cognition. Overall adverse events were similar to placebo, while somnolence was more frequent with cannabinoids (RR 2.03, 95% CI 1.29-3.20). CONCLUSIONS: Cannabinoid-based therapies do not improve agitation, neuropsychiatric symptoms, or cognition in Alzheimer's disease and increase somnolence.
Humans↗
Individualising gentamicin dosage regimens. A comparative review of selected models, data fitting methods and monitoring strategies.
The various components required for individualising clinical drug dosage regimens are reviewed, including a study of 3 types of fitting procedures, 2 types of gentamicin pharmacokinetic model and the utility of D-optimal times for obtaining serum gentamicin concentrations. The combination of the current Bayesian fitting procedure, the kslope pharmacokinetic model [in which the elimination rate constant (kel) can change from dose to dose with changing creatinine clearance] and the explicit measurement of the assay error pattern yielded predictions of future serum gentamicin concentrations which were (a) slightly better than those found using weighted nonlinear least squares; (b) somewhat better than those found with Bayesian fitting and a fixed-kel model; (c) better than those found using the traditional linear regression fitting procedure and a fixed kel model. D-Optimally timed pairs of concentrations also predicted future concentrations at least as well, and more cost effectively.
Data Interpretation, Statistical↗