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PubMed2005

Using prior distributions to synthesize historical evidence: comments on the Goodman-Sladky case study of IVIg in Guillain-Barré syndrome.

One feature of the Bayesian approach is that it provides methods for synthesizing what is known about a question of interest and provides a formalism based on the laws of probability for incorporating this auxiliary knowledge into the planning and the analysis of the next study. In this comment, we use elements of the Goodman-Sladky case study to illustrate (1) the use of Bayesian methods to quantify historical information about an intervention through the specification of a prior distribution, (2) an approach to the analysis of the sensitivity of the conclusions of a Bayesian analysis to the specification of the prior distribution, and (3) we comment on the role of research synthesis for combining information about an intervention from different data sources as a tool to help summarize evidence about the intervention.

Adult↗

PubMed2025

A novel second-generation HIV-1 circulating recombinant form (CRF183_0107) identified among men who have sex with men in China.

OBJECTIVE: This study aimed to report a novel HIV-1 circulating recombinant form (CRF) identified among men who have sex with men (MSM) in China. DESIGN: Viral sequences were isolated from MSM patients, and the recombination and evolutionary histories of this CRF were elucidated through phylogenetic and Bayesian analyses. METHODS: Near full-length genomes (NFLGs) and partial genome sequences were amplified from RNA extracted from plasma samples of three HIV-1 seropositive MSM in Heilongjiang Province, China. Phylogenetic analysis was conducted using FastTree v2.1.9, and recombination analysis was performed using Simplot v3.5.1. The emergence time of the novel CRF was estimated by Bayesian evolutionary analysis using BEAST v1.10.4. RESULTS: Two NFLGs and two partial genome segments were successfully obtained from three MSM participants. This novel CRF was characterized by 12 mosaic gene segments, comprising 6 segments from the CRF01_AE cluster 4 and 6 segments from the CRF07_BC cluster N, and thus was designated as CRF183_0107. The estimated time of origin for the CRF01_AE and CRF07_BC components within CRF183_0107 were approximately 2009.2 and 2012.5, respectively. CONCLUSION: A novel second-generation HIV-1 recombinant, named CRF183_0107, was identified within the MSM population in China. This CRF exemplified the recombination events occurring between the CRF01_AE cluster 4 and the CRF07_BC cluster N during 2009-2012.

Humans↗

PubMedDate not supplied

Reliability analysis of DOOF for Weibull distribution.

Hierarchical Bayesian method for estimating the failure probability p(i) under DOOF by taking the quasi-Beta distribution B(p(i-1), 1, 1, b) as the prior distribution is proposed in this paper. The weighted Least Squares Estimate method was used to obtain the formula for computing reliability distribution parameters and estimating the reliability characteristic values under DOOF. Taking one type of aerospace electrical connector as an example, the correctness of the above method through statistical analysis of electrical connector accelerated life test data was verified.

Equipment Failure↗

PubMed2026

Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy.

Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1-13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14-17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy's central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18-20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola1,21, Zika2, SARS-CoV-2 (ref. 22), mpox3,4 and H5N1 (refs. 23,24), we demonstrate state-of-the-art accuracy with up to 2-3 orders of magnitude improvements in speed. Assessing Delphy's scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user's machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response.

Journal Article↗

PubMed2001

Marker selection by Akaike information criterion and Bayesian information criterion.

We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.

Adult↗

PubMed2006

A Bayesian approach for population pharmacokinetic modelling of sirolimus.

AIMS: To explore a Bayesian approach for the pharmacokinetic analysis of sirolimus concentration data arising from therapeutic drug monitoring (poorly informative concentration-time point design), and to explore possible covariate relationships for sirolimus pharmacokinetics. METHODS: Sirolimus concentration-time data were available as part of routine clinical care from 25 kidney transplant recipients. Most samples were taken at or near the trough time point at steady state. The data were analyzed using a fully conditional Bayesian approach with PKBUGS (v 1.1)/WinBUGS (v 1.3). Features of the data included noncompliance and missing concentration measurements below the limit of sensitivity of the assay. Informative priors were used. RESULTS: A two-compartment model with proportional residual error provided the best fit to the data (consisting of 315 sirolimus concentration-time points). The typical value for the apparent clearance (CL/F ) was 12.5 l h(-1) at the median age of 44 years. Apparent CL was found to be inversely related to age with a posterior probability of a clinically significant effect of 0.734. CONCLUSIONS: A population pharmacokinetic model was developed for sirolimus using a novel approach. Bayesian modelling with informative priors allowed interpretation of a significant covariate relationship, even using poorly informative data.

Adult↗

PubMed2002

Advanced methods in meta-analysis: multivariate approach and meta-regression.

This tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood, we discuss methods to analyse univariate as well as bivariate treatment effects in meta-analyses as well as meta-regression methods. Several extensions of the models are discussed, like exact likelihood, non-normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta-analysis. All methods are illustrated by a meta-analysis concerning the efficacy of BCG vaccine against tuberculosis. All analyses that use approximate likelihood can be carried out by standard software. We demonstrate how the models can be fitted using SAS Proc Mixed.

BCG Vaccine↗

PubMed2003

Hierarchical Bayesian spatial modelling of small-area rates of non-rare disease.

We present Bayesian hierarchical spatial models for the analysis of the geographical distribution of a non-rare disease or event. The work is motivated by the need for ascertaining regional variations in health services outcomes and resource use and for assessing the potential sources of these variations. The models discussed herein readily accommodate random spatial effects and covariate effects. We discuss Bayesian inferential framework and implementation of a hybrid Markov chain Monte Carlo method for full Bayesian model inference. The methods are illustrated through an analysis of regional variation in chronic lung disease (CLD) rates among neonatal intensive care unit (NICU) patients across Canada. Specifically, we first present a random effects binomial model for spatially correlated CLD rates, with random spatial effects accounting for latent or covariate effects. These random spatial effects depict regional or spatial variation in chronic lung disease occurrence. We then extend this model to include covariates. With this extension, we assess residual spatial effects and the extent to which risk factors such as illness severity at NICU admission, low birth weight, and very low birth weight influence the CLD rate variation.

Bayes Theorem↗

PubMed2005

A novel approach for clustering proteomics data using Bayesian fast Fourier transform.

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Algorithms↗

PubMed2002

Rise in malaria incidence rates in South Africa: a small-area spatial analysis of variation in time trends.

Using Bayesian statistical models, the authors investigated spatial and temporal variations in small-area malaria incidence rates for the period mid-1986 to mid-1999 for two districts in northern KwaZulu Natal, South Africa. Maps of spatially smoothed incidence rates at different time points and spatially smoothed time trends in incidence gave a visual impression of the highest increase in incidence occurring where incidence rates previously had been lowest. This was confirmed by conditional autoregressive models, which showed that there was a significant negative association between time trends and smoothed baseline incidence before the steady rise in caseloads began. Growth rates also appeared to be higher in the areas close to the Mozambican border. The main findings of this analysis were that: 1) the spatial distribution of the rise in malaria incidence is uneven and strongly suggests a geographic expansion of high-malaria-risk areas; 2) there is evidence of a stabilization of incidence in areas that had the highest rates before the current escalation of rates began; and 3) areas immediately adjoining the Mozambican border appear to have undergone larger increases in incidence, in contrast to the general pattern of low growth in the more northern, high-baseline-incidence areas, but this was not confirmed by modeling. Smoothing of small-area maps of incidence and growth in incidence (trend) is important for interpretation of the spatial distribution of disease incidence and the spatial distribution of rapid changes in disease incidence.

Bayes Theorem↗

PubMed2002

Computerized lesion detection on breast ultrasound.

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.

Bayes Theorem↗

PubMedDate not supplied

Impact of generic drug entry on cost-effectiveness analysis.

The market share of generic drugs has grown substantially since the passage of the Waxman-Hatch Act, increasing from 19% in 1984 to 50% in 2001. At entry, the generic drugs typically are priced lower than brand-name drugs; thus, incorporating the impact of the generic drug entry introduces an additional source of uncertainty in economic models as both the timing of entry and the level of generic drug pricing are subject to variation. In this article, the authors explored the impact of generic drug entry on cost-effectiveness analyses of new or brand-name drugs. Using a mathematical model, they argue that failure to incorporate the impact of generic drug entry will underestimate the incremental cost-effectiveness ratio (ICER) and, thus, overstate the economic benefit of the new product. The authors provide 2 examples to illustrate such impact on short-term and long-term cost-effectiveness analyses. To better assess the uncertainty associated with the impact of generic drug entry, in addition to a deterministic analysis, they also employed a Bayesian probabilistic approach to analyze these examples and presented the results using cost-effectiveness acceptability curves. They conclude that incorporating generic drug entry into pharmacoeconomic models will yield more accurate projections of the ICER and lead to better decision making.

Bayes Theorem↗

PubMed2006

Evaluation of antiangiogenic treatment effects on tumors' microcirculation by Bayesian physiological pharmacokinetic modeling and magnetic resonance imaging.

A physiological pharmacokinetic (PBPK) model was used to estimate tumor microcirculation in nude mice with a grafted tumor. The kinetics of a rapid clearance blood pool agent, Vistarem, were investigated by dynamic MRI after bolus administration. Signal enhancements were recorded in arterial blood and in tumor tissue. To analyze these data, we developed a whole-body mathematical model of the agent's biodistribution using physiological parameters. The model included six compartments: arterial and venous plasma, tumor (split into capillaries and interstitium), and the rest of the body (also split into capillaries and interstitium). As an application, changes in tumor microcirculation parameters were evaluated in mice receiving either an antiangiogenic treatment (ZD4190) or a placebo. The analysis was performed in a Bayesian framework, and the model was fitted to experimental data using Markov Chain Monte Carlo techniques. Results showed a significant difference in tumor microcirculation between the two groups of mice when the microcirculation parameters are considered together. This whole-body physiological model enables to analyze jointly data in tumor tissue and in arterial blood. This leads to accurate estimates of microcirculation parameters and the evaluation of their uncertainty.

Animals↗

PubMed2025

Navigating Sampling Bias in Discrete Phylogeographic Analysis: Assessing the Performance of an Adjusted Bayes Factor.

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modeling.

Bayes Theorem↗

PubMed2005

A nuclear DNA phylogenetic perspective on the evolution of echolocation and historical biogeography of extant bats (chiroptera).

Bats (Order Chiroptera), the only mammals capable of powered flight and sophisticated laryngeal echolocation, represent one of the most species-rich and ubiquitous orders of mammals. However, phylogenetic relationships within this group are poorly resolved. A robust evolutionary tree of Chiroptera is essential for evaluating the phylogeny of echolocation within Chiroptera, as well as for understanding their biogeographical history. We generated 4 kb of sequence data from portions of four novel nuclear intron markers for multiple representatives of 17 of the 18 recognized extant bat families, as well as the putative bat family Miniopteridae. Three echolocation-call characters were examined by mapping them onto the combined topology: (1) high-duty cycle versus low-duty cycle, (2) high-intensity versus low-intensity call emission, and (3) oral versus nasal emission. Echolocation seems to be highly convergent, and the mapping of echolocation-call design onto our phylogeny does not appear to resolve the question of whether echolocation had a single or two origins. Fossil taxa may also provide insight into the evolution of bats; we therefore evaluate 195 morphological characters in light of our nuclear DNA phylogeny. All but 24 of the morphological characters were found to be homoplasious when mapped onto the supermatrix topology, while the remaining characters provided insufficient information to reconstruct the placement of the fossil bat taxa with respect to extant families. However, a morphological synapomorphy characterizing the Rhinolophoidea was identified and is suggestive of a separate origin of echolocation in this clade. Dispersal-Vicariance analysis together with a relaxed Bayesian clock were used to evaluate possible biogeographic scenarios that could account for the current distribution pattern of extant bat families. Africa was reconstructed as the center of origin of modern-day bat families.

Amino Acid Sequence↗

PubMed2003

Use of gene networks for identifying and validating drug targets.

We propose a new method for identifying and validating drug targets by using gene networks, which are estimated from cDNA microarray gene expression profile data. We created novel gene disruption and drug response microarray gene expression profile data libraries for the purpose of drug target elucidation. We use two types of microarray gene expression profile data for estimating gene networks and then identifying drug targets. The estimated gene networks play an essential role in understanding drug response data and this information is unattainable from clustering methods, which are the standard for gene expression analysis. In the construction of gene networks, we use the Bayesian network model. We use an actual example from analysis of the Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information to drug discovery.

Algorithms↗

PubMed2002

Bayesian reconstruction of sound localization cues from responses to random spectra.

The directionally sensitive acoustics of the pinnae enable humans to perceive the up-down and front-back direction of sound. This mechanism complements another, independent mechanism that derives sound-source azimuth from interaural difference cues. The pinnae effectively add direction-dependent spectral notches and peaks to the incoming sound, and it has been shown that such features are used to code sound direction in the median plane. However, it is still unclear which of the pinna-induced features play a role in sound localization. The present study presents a method for the reconstruction of the spatially relevant features in the spectral domain. Broadband sounds with random spectral shapes were presented in rapid succession as subjects made saccadic eye movements toward the perceived stimulus locations. The analysis, which is based on Bayesian statistics, indicates that specific spectral features could be associated with perceived spatial locations. Spectral features that were determined by this psychophysical method resemble the main characteristics of the pinna transfer functions obtained from acoustic measurements in the ear canal. Despite current experimental limitations, the approach may prove useful in the study of perceptually relevant spectral cues underlying human sound localization.

Acoustic Stimulation↗

PubMed2006

Comparative study of Kaposi's sarcoma-associated herpesvirus serological assays using clinically and serologically defined reference standards and latent class analysis.

Accurate determination of infection with Kaposi's sarcoma-associated herpesvirus (KSHV) has been hindered by the lack of a "gold standard" for comparison of serological assays used to estimate KSHV prevalence in serosurveys conducted in different settings. We have evaluated the performance of five in-house (developed at University College London [UCL], United Kingdom, and at the virology laboratory of the Instituto de Medicine Tropical [IMT] in Sao Paulo, Brazil) and two commercial (ABI and DIAVIR) serological assays to detect antibodies to latency-associated nuclear antigen (LANA) and to lytic KSHV antigens. We used a variety of serum samples assembled to represent populations likely to be at high, intermediate, and low risk of KSHV infection in Brazil. Composite reference standard panels were prepared based on clinical and serological parameters, against which assay performances were assessed using conventional Bayesian statistics and latent class analysis (LCA). Against the clinical reference standard, in-house immunofluorescence assays to detect anti-LANA antibodies (IFA-LANA) produced at UCL and IMT had similar performances, with sensitivities of 61% (95% confidence interval [CI], 48% to 74%) and 72% (95% CI, 58% to 83%) and specificities of 99% (95% CI, 94% to 100%) and 100% (95% CI, 96% to 100%), respectively, and only the IMT IFA-LANA was included in LCA, together with the IMT IFA-lytic and four enzyme-linked immunosorbent assays (ELISAs). The LCA indicated that the IMT whole-virus ELISA performed best (sensitivity, 87% [95% CI, 81% to 91%]; and specificity, 100% [95% CI, 98% to 100%]), confirming the results obtained with the conventional statistical approach. Commercially available ELISA-based tests yielded the lowest specificities using a spectrum of serum samples. The evaluation of KSHV serological assays is warranted before planning serosurveys in various settings.

Antibodies, Viral↗