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Results for “Bayesian modelling”

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At least 19 recordsLinked to original sources

PubMed1991

Everyday diagnostics--a critique of the Bayesian model.

In recent years Bayesian probability calculus and Bayesian decision procedures have been recommended for use in clinical medicine. The author investigate everyday diagnostics asking if it takes place in a reality that meets the conditions of the method. He finds it does not. Above all we lack that strict randomness essential for probability calculus and, further, evaluation of utility easily becomes disputable, not to say unethical. The measures constructed for neutralizing these discrepancies between model and reality demand great resources. Yet, they are not enough to permit the clinician to refrain from supervising the consequences of his decisions as carefully as he has always done. A different method, a different model is wanted.

Bayes Theorem↗

PubMedDate not supplied

Evaluation of a computerized Bayesian model for diagnosis of renal cyst vs. tumor vs. normal variant from urogram information.

The diagnostic problem of cyst/tumor/normal variant raised on an excretory urogram leads to a decision to do needle aspiration or renal arteriography. This decision depends critically upon the probability distribution for the three diagnoses. A computerized Bayesian model of a uroradiologist's diagnostic process in solving the problem was developed. The model was based on subjective probabilities supplied by an experienced uroradiologist. The model was evaluated in terms of its ability to decrease the cost of further diagnosis regarding aspiration versus arteriography. The model's output was compared with decisions made by unaided radiologists viewing the same panel of 50 urogram test cases. Results indicate that the model does not improve upon the decisions made by a radiologist highly experienced with this diagnostic problem. However, the decisions made by unaided, less experienced radiologists result in greater cost than those of the model.

Angiography↗

PubMed2025

Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.

With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a "lack of information." In cancer research and clinical practice, a source of information that is broadly available and highly cost-effective comes from pathological images, which are routinely collected for definitive diagnosis and staging. In this article, we consider a Bayesian approach for selecting relevant genetic variables and modeling their relationships with a cancer outcome/phenotype. We propose borrowing information from (manually curated, low-dimensional) pathological imaging features via reinforcing the same selection results for the cancer outcome and imaging features. We further develop a weighting strategy to accommodate the scenario where information borrowing may not be equally effective for all subjects. Computation is carefully examined. Simulations demonstrate competitive performance of the proposed approach. We analyze TCGA (The Cancer Genome Atlas) LUAD (lung adenocarcinoma) data, with overall survival and gene expressions being the outcome and genetic variables, respectively. Findings different from the alternatives and with sound properties are made.

Humans↗

PubMed1975

The impact of laboratory error on the normal range: a Bayesian model.

Interpretation of clinical laboratory results, aside from clinical considerations, is based on the probability of the result being within a given normal range. This probability is influenced by the degree of error inherent in the analytical method. It would be advantageous to assign a more definite probability to the result of the measurement by combining the error distribution of the result around the true value and the distribution of the healthy population that serves as a reference. Bayesian statistics permits the revision of this prior information into a single probability.

Calcium↗

PubMed1992

Bayesian model selection and minimum description length estimation of auditory-nerve discharge rates.

Auditory-nerve fiber discharges are modeled as self-exciting point processes with intensity given by the product of a stimulus-related function and a refractory-related function. Previous methods of estimating these two functions, based on the maximum-likelihood principle, have the problem of estimating more parameters than the data can support. A new procedure, based on a Bayes criterion for choosing the complexity of the model in addition to estimating the parameters, solves the over-parametrization problem. This procedure is seen to relate asymptotically to Rissanen's minimum description length (MDL) criterion. A performance comparison of the MDL procedure with previous maximum-likelihood algorithms promotes the adoption of the MDL procedure for simultaneous estimation of the stimulus and recovery properties of auditory-nerve discharge.

Algorithms↗

PubMed1991

Early individualization of tricyclic antidepressant dosing using a Bayesian pharmacokinetic model.

Existing methods to prospectively dose tricyclic antidepressants (TCAs) require either specific test doses, precisely timed serum sampling, or both. We prospectively tested a new pharmacokinetic model that allows flexible dosing and sampling to determine maintenance requirements in patients receiving TCAs. Thirty-four patients entered the study. Drug concentrations were measured on the third day after starting TCA therapy. These values were analyzed using a Bayesian pharmacokinetic model to determine drug clearance and volume of distribution. This information was then used to predict the serum concentration resulting from a maintenance dose chosen by the psychiatrist. In phase I (n = 17), patients received imipramine without specific starting doses. Phase II (n = 17) was performed to provide a preliminary evaluation of the method in the usual clinical environment. In this phase, patients received either amitriptyline, imipramine, desipramine, doxepin (75 mg on day 1,100 mg on day 2), or nortriptyline (50 mg on day 1, 75 mg on day 2). Lower doses were allowed if clinically indicated. The predictability of future serum concentrations was then compared between the two phases. The mean prediction errors (model bias) in phases I and II were -15.5 +/- 27.3 and -12.3 +/- 21.8 ng/mL and were not different (p greater than 0.05). The absolute prediction errors (model precision) were 18.5 +/- 25.1 and 18.8 +/- 16.0 ng/mL and were not different (p greater than 0.05). Two slow metabolizers were identified (clearance less than 0.10 L/kg/h). This new method allows the determination of maintenance dose requirements early in therapy without standard test doses or specifically timed serum sampling.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed2026

Genetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores.

OBJECTIVES: Incisional hernia (IH) affects 13-30% of people after abdominal surgery, resulting in substantial morbidity and costs. While clinical risk factors have been studied extensively, genomic risk for IH is incompletely understood. We aimed to evaluate the impact of polygenic risk scores (PRS) on IH risk prediction. METHODS: We created and evaluated three PRS for abdominal hernia, ventral hernia and latent hernia susceptibility for prediction of IH in an institutional biobank. The primary outcome was defined as the diagnosis or repair of an IH based on ICD-9/10-CM/PCS and CPT codes. Clinical covariates included age, sex, body mass index (BMI), smoking status, index procedure type, and perioperative surgical site infection. A phenome-wide association study (PheWAS) was performed to assess clinical associations with increased PRS. We then tested the ability of the PRS to improve prediction for IH by modeling clinical covariates with and without PRS in patients who underwent abdominal surgery. Model performance was assessed using 10 iterations of 5-fold cross-validation to estimate Brier scores and area under the receiver operating characteristic curve (AUROC), which were compared using cross-model Bayesian analysis of variance. RESULTS: In 55,809 subjects, assessed PRS was significantly associated with incisional, umbilical, and ventral hernia on PheWAS, with 1.19 greater odds of developing IH per 1-SD increase in PRS (95% CI: 1.13-1.25, P < 0.001). Of 9,909 subjects who underwent qualifying abdominal surgery, 706 developed IH. In this cohort, the latent hernia susceptibility PRS was associated with a 16% increased hazard of developing IH per 1-SD increase (HR 1.16; 95% CI: 1.07-1.26; P < 0.001). Compared to a predictive model using clinical covariates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC = 0.660, 95% CI: 0.653-0.666), addition of the PRS showed similar Brier score and AUROC estimates (Brier score = 0.047, 95% CI: 0.046-0.048; AUROC: 0.667, 95% CI: 0.661-0.673) at five years. Cross-model Bayesian analysis demonstrated >99% probability of practical equivalence when trying to detect a difference of ≥ 0.02. CONCLUSION: All three PRS for hernia were independently associated with IH, suggesting that genomic factors contribute significantly to IH development. However, none of the three PRS meaningfully improved clinical IH risk prediction in patients who underwent abdominal surgery. This suggests that clinical comorbidities and surgical techniques may be equally as important as genomic architecture.

Bayesian analysis↗

PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗

PubMed2026

On the origin of animals and placental mammals: a critique of literalist readings of the fossil record.

The fossil record is incomplete, as evidenced by the pervasive presence of ghost lineages throughout the Tree of Life. For example, across placental mammals, at least 720 Myr of basal lineages are ghost lineages, that is, lineages that have left no fossil evidence of their past history. In contrast, some studies have suggested that the fossil record is a faithful temporal archive of evolutionary history and thus the times of diversification of clades must be close to the ages of their oldest fossils. Such literalist interpretations have been contradicted by analysis of molecular datasets which, in many cases, indicate that groups including placental mammals and animals may have originated at times substantially older than their fossil records. Some of those studies have further argued that, in the case of animals and placental mammals, molecular clocks are uninformative, suffer from characteristic pathologies, and thus cannot distinguish between recent and ancient hypotheses of diversification. Here, we reexamine these two cases and show, using Bayesian model selection theory, that the explosive diversification models previously proposed for animals and placental mammals have a posterior probability of ∼0. We show the characteristic pathologies purportedly discovered do not exist, highlight errors in previous analyses, and provide advice on best practice for molecular-clock dating analysis.

Animals↗

PubMed2026

Weathering the storm: Most maternal and environmental drivers of individual reproductive success do not scale up to population recruitment in a large herbivore.

Population growth depends upon individual survival and reproduction, but do drivers of individual reproductive success scale up to population recruitment? Factors affecting individuals may have little effect on population dynamics if individuals within a population experience different conditions. When seasonal resource availability is unpredictable and breeding season long, average conditions over a breeding cycle may poorly reflect the environment experienced by many individuals. We compared the drivers of individual reproductive success and population recruitment in an asynchronously breeding large herbivore, the eastern grey kangaroo (Macropus giganteus). We analysed 18 years of individual-based data using multivariate hierarchical Bayesian models to first identify the causal mechanisms relating population density, environmental conditions and maternal traits to individual success. We then assessed whether the drivers of individual reproductive success scaled up to determine population recruitment. Most maternal and environmental covariates strongly influenced individual reproductive success, with distinct effects on juvenile survival before and after pouch exit. Maternal traits had a greater influence in the pouch, whereas environmental conditions became increasingly important once young exited the pouch. Most drivers of individual reproductive success did not affect population recruitment. Recruitment increased with population density and mean body condition of adult females. Weather harshness had a weak positive effect on recruitment, which appeared independent of female age structure, previous recruitment or forage. Most drivers of individual reproductive success did not scale up to population recruitment. Birth asynchrony could buffer population recruitment against environmental variation such that variables affecting individual reproduction have little impact at the population level. Large herbivores that reproduce asynchronously may therefore be more resilient to environmental variability than synchronous breeders.

Bayesian modelling↗

PubMed1991

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.

Algorithms↗

PubMed1992

[Data analysis by statistical models].

The basic idea for the realization of effective statistical data analysis is illustrated with an example. The use of statistical models is explained and the feasibility of objective comparison of the models by an information criterion AIC is demonstrated. Further, the possibility of practical use of Bayesian models for complex data analysis is explained. Finally, the necessity of cooperation between the experts of respective fields and statisticians for further development of statistical data analysis is mentioned.

Adult↗

PubMed1992

Ranitidine pharmacokinetics and adverse central nervous system reactions.

BACKGROUND: Treatment with histamine2-receptor antagonists has been associated with adverse central nervous system reactions (CNS-ADRs). Previous studies of cimetidine have shown an association between CNS-ADRs and high cimetidine drug levels. While case reports of ranitidine CNS-ADRs have appeared, we wanted to study a series of patients, some of whom were critically ill, for the presence of CNS-ADRs and to correlate these with ranitidine pharmacokinetics. METHODS: A prospective, observational, open study included 163 consecutive patients, of whom 41 met entry criteria. A nonlinear least-squares regression analysis was used to establish a ranitidine pharmacokinetic dosing model. Ranitidine levels were determined by a high-performance liquid chromatographic assay. Individual ranitidine pharmacokinetics were determined by means of a bayesian model. Observations on 13 possible CNS-ADRs were recorded. The CNS-ADRs were evaluated by the Naranjo rating system. RESULTS: Ranitidine-associated CNS-ADRs, particularly lethargy, confusion, somnolence, and disorientation, occurred more frequently in patients with renal function impairment, and these were associated with higher peak concentrations, average plasma concentrations, and area under the curve. CONCLUSIONS: Ranitidine, when given in conventional doses, can cause CNS-ADRs, particularly in older patients who have substantial renal function impairment. These CNS-ADRs occur as a consequence of altered ranitidine disposition. Ranitidine doses should be reduced when renal function impairment is present, and patients should be carefully observed for CNS-ADRs.

Aged↗

PubMed2025

Inferring the sensitivity of wastewater metagenomic sequencing for early detection of viruses: a statistical modelling study.

BACKGROUND: Metagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We aimed to quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. METHODS: In this statistical modelling study, we analysed sequencing data from four studies of untargeted W-MGS to estimate the relative abundance of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public health reports. We combined these estimates using a hierarchical Bayesian model to predict relative abundance at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridisation capture enrichment panel. FINDINGS: After controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponded to a predicted SARS-CoV-2 relative abundance ranging from 3·8 × 10-10 to 2·4 × 10-7 across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies greatly increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 27-fold (from US$7·87 million to $287 000) and 29-fold (from $1·98 million to $69 100) for a system able to detect a SARS-CoV-2-like pathogen before reaching 0·01% cumulative incidence. INTERPRETATION: The large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and laboratory protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well chosen hybridisation capture panels can greatly increase sensitivity and reduce cost for viruses in the panel, but might reduce sensitivity to unknown or unexpected pathogens. FUNDING: The Wellcome Trust, Open Philanthropy, and Musk Foundation.

Humans↗

PubMed2025

BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.

MOTIVATION: statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. RESULTS: We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100× more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION: Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

Genome-Wide Association Study↗

PubMed2026

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Over 75 Alzheimer's disease (AD) and dementia-associated variants have been identified through genome-wide association studies, but the utility of polygenic risk scores (PRS) for predicting AD and dementia in diverse and admixed populations remains unclear. We compared how PRS approaches differing in p-value thresholds, variant weights, and source ancestry perform in predicting dementia in 6338 African American, Chinese, Hispanic, and White individuals from the Multi-Ethnic Study of Atherosclerosis. We tested clumping and thresholding (C+T) methods with varying parameters against Bayesian approaches (PRS-CS, PRS-CSx). We compared the ability of each method to predict incident dementia in all participants and in groups stratified by self-reported race/ethnicity. We additionally analyzed performance across groups stratified by estimated proportion of non-Finnish European (NFE)-like ancestry. Including more variants does not improve performance. We found comparable associations between dementia and PRS when comparing a C+T method with only 15 SNPs and PRS derived from Bayesian models that include > 800,000 SNPs (HR5e-08 = 1.18, 95% CI: 1.08-1.28; HRCSx = 1.17, 95% CI: 1.07-1.27). The p < 5e-08 C+T method was more strongly associated with incident dementia in populations genetically dissimilar from the source data (HRlowNFE_5e-08 = 1.27, 95% CI: 1.08-1.50; HRlowNFE_CSx = 1.12, 95% CI: 0.94-1.33). More selective PRS models using genome-wide significant SNPs may be preferable for dementia prediction in diverse populations.

Aged↗

PubMed1991

Ruling out acute myocardial infarction. A prospective multicenter validation of a 12-hour strategy for patients at low risk.

BACKGROUND: Although previous investigations have suggested that 24 hours is required to exclude acute myocardial infarction in patients who are admitted to a coronary care unit for the evaluation of acute chest pain, we hypothesized that a 12-hour period might be adequate for patients with a low probability of infarction at the time of admission. METHODS: Using a Bayesian model, we developed a strategy to identify candidates for a shorter period of observation from an analysis of a derivation set of 976 patients with acute chest pain who were admitted to three teaching and four community hospitals. In the derivation set, patients whose clinical characteristics in the emergency room predicted a low (less than or equal to 7 percent) probability of myocardial infarction had only a 0.4 percent risk of infarction if they had neither abnormal levels of cardiac enzymes nor recurrent ischemic pain during the first 12 hours of hospitalization. In an independent testing set of 2684 patients from the seven hospitals, 957 admitted patients (36 percent) were classified as candidates for this 12-hour period of observation according to a previously published multivariate algorithm. Few of these patients were actually transferred from a monitored setting at 12 hours. RESULTS: Of the 771 candidates for a 12-hour period of observation who did not have enzyme abnormalities or recurrent pain during the first 12 hours, 4 (0.5 percent) were subsequently found to have acute myocardial infarction, and only 3 (0.4 percent) died after primary cardiac arrests, all of which occurred three to five days after admission. Rates of other major cardiovascular complications were low in the patients who might have been transferred from the coronary care unit after 12 hours with this strategy. In patients with a higher initial risk of infarction, the standard strategy of 24-hour observation identified all but 11 of 739 acute myocardial infarctions (1 percent). CONCLUSIONS: Emergency room clinical data can be used to identify a large subgroup of patients for whom a 12-hour period of observation is normally sufficient to exclude acute myocardial infarction. Patient-specific evaluation and treatment can then proceed without the restrictions imposed by "rule-out" protocols for myocardial infarction.

Adult↗

PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗