Bayesian modeling approach to examining heterogeneity in censored survival analysis.
Explore the source record for details and available documents.
Bayes Theorem↗
SEARCH · PubMed Health
Results for “Bayesian analysis”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
[Exploratory-confirmative perspective in biomedical applications of statistics: 2 dialogues (I). Bayesianism versus frequentialism: their respective practical implications on data analysis].
Explore the source record for details and available documents.
Bayes Theorem↗
Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information.
We predict the operon structure of the Bacillus subtilis genome using the average operon length, the distance between genes in base pairs, and the similarity in gene expression measured in time course and gene disruptant experiments. By expressing the operon prediction for each method as a Bayesian probability, we are able to combine the four prediction methods into a Bayesian classifier in a statistically rigorous manner. The discriminant value for the Bayesian classifier can be chosen by considering the associated cost of misclassifying an operon or a non-operon gene pair. For equal costs, an overall accuracy of 88.7% was found in a leave-one-out analysis for the joint Bayesian classifier, whereas the individual information sources yielded accuracies of 58.1%, 83.1%, 77.3%, and 71.8% respectively. The predicted operon structure based on the joint Bayesian classifier is available from the DBTBS database (http://dbtbs.hgc.jp).
Bacillus subtilis↗
Hyperbaric oxygen for carbon monoxide poisoning : a systematic review and critical analysis of the evidence.
Poisoning with carbon monoxide (CO) is an important cause of unintentional and intentional injury worldwide. Hyperbaric oxygen (HBO) enhances CO elimination and has been postulated to reduce the incidence of neurological sequelae. These observations have led some clinicians to use HBO for selected patients with CO poisoning, although there is considerable variability in clinical practice. This article assesses the effectiveness of HBO compared with normobaric oxygen (NBO) for the prevention of neurological sequelae in patients with acute CO poisoning. The following databases were searched: MEDLINE (1966 to present), EMBASE (1980 to present), and the Controlled Trials Register of the Cochrane Collaboration, supplemented by a manual review of bibliographies of identified articles and discussion with recognised content experts. All randomised controlled trials involving people acutely poisoned with CO, regardless of severity, were examined. The primary analysis included all trials from which data could be extracted. Sensitivity analysis examined trials with better validity (defined using the validated instrument of Jadad) and those enrolling more severely poisoned patients. Two reviewers independently extracted from each trial, including information on the number of randomised patients, types of participants, the dose and duration of the intervention, and the prevalence of neurological sequelae at follow-up. A pooled odds ratio (OR) for the presence of neurological symptoms at 1-month follow-up was calculated using a random effects model. Bayesian models were also investigated to illustrate the degree of certainty about clinical effectiveness. Eight randomised controlled trials were identified. Two had no evaluable data and were excluded. The remaining trials were of varying quality and two have been published only as abstracts. The severity of CO poisoning varied among trials. At 1-month follow-up after treatment, sequelae possibly related to CO poisoning were present in 242 of 761 patients (36.1%) treated with NBO, compared with 259 of 718 patients (31.8%) treated with HBO. Restricting the analysis to the trials with the highest quality scores or those that enrolled all patients regardless of severity did not change the lack of statistical significance in the outcome of the pooled analysis. We found empiric evidence of multiple biases that operated to inflate the benefit of HBO in two positive trials. In contrast, the interpretation of negative trials was hampered by low rates of follow-up, unusual interventions for control patients and inclusion of less severely poisoned patients. Collectively, these limitations may have led negative trials to overlook a real and substantial benefit of HBO (type II error). There is conflicting evidence regarding the efficacy of HBO treatment for patients with CO poisoning. Methodological shortcomings are evident in all published trials, with empiric evidence of bias in some, particularly those that suggest a benefit of HBO. Bayesian analysis further illustrates the uncertainty about a meaningful clinical benefit. Consequently, firm guidelines regarding the use of HBO for patients with CO poisoning cannot be established. Further research is needed to better define the role of HBO, if any, in the treatment of CO poisoning. Such research should not exclude patients with severe poisoning, have a primary outcome that is clinically meaningful and have oversight from an independent data monitoring and ethics committee.
Carbon Monoxide Poisoning↗
Bayesian statistics in medical research: an intuitive alternative to conventional data analysis.
Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a 'prior distribution' that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying 'no effect'. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).
Bayes Theorem↗
Discriminant analysis algorithm based on a distance function and on a Bayesian decision.
We propose a new algorithm for the allocation of an individual to one of several possible groups or populations. The algorithm enables us to define a finite partition over the sample space, based on distance function. This partition is used, jointly with the application of a standard Bayesian decision rule, to allocate individuals to the populations. The algorithm also provides a measure of the allocation confidence for each individual, in a similar manner to that of logistic regression. The error rates for classification are also computed using the leave-one-out method. Results are compared with those obtained with other discriminant analysis techniques previously reported: Fisher's linear discriminant function, the quadratic discriminant function, logistic discrimination, and others.
Adolescent↗
Genetic analysis of somatic cell scores in US Holsteins with a Bayesian mixture model.
The objective of this study was to apply finite mixture models to field data for somatic cell scores (SCS) for estimation of genetic parameters. Data were approximately 170,000 test-day records for SCS from first-parity Holstein cows in Wisconsin. Five different models of increasing level of complexity were fitted. Model 1 was the standard single-component model, and the others were 2-component Gaussian mixtures consisting of similar but distinct linear models. All mixture models (i.e., 2 to 5) included separate means for the 2 components. Model 2 assumed entirely homogeneous variances for both components. Models 3 and 4 assumed heterogeneous variances for either residual (model 3) or genetic and permanent environmental variances (model 4). Model 5 was the most complex, in which variances of all random effects were allowed to vary across components. A Bayesian approach was applied and Gibbs sampling was used to obtain posterior estimates. Five chains of 205,000 cycles were generated for each model. Estimates of variance components were based on posterior means. Models were compared by use of the deviance information criterion. Based on the deviance information criterion, all mixture models were superior to the linear model for analysis of SCS. The best model was one in which genetic and PE variances were heterogeneous, but residual variances were homogeneous. The genetic analysis suggested that SCS in healthy and infected cattle are different traits, because the genetic correlation between SCS in the 2 components of 0.13 was significantly different from unity.
Animals↗
Evaluation of a new antibody-based enzyme-linked immunosorbent assay for the detection of bovine leukemia virus infection in dairy cattle.
The objective of this study was to validate a new blocking enzyme-linked immunosorbent assay (ELISA) (designated M108 for milk and S108 for serum samples) for detecting bovine leukemia virus (BLV) infection in dairy cattle. Milk, serum, and ethylenediaminetetraacetic acid-blood samples were collected from 524 adult Holstein cows originating from 6 dairy herds in Central Argentina. The M108 and S108 were compared with agar gel immunodiffusion (AGID), polymerase chain reaction and a commercial ELISA. Because there is currently no reference test capable of serving as a gold standard, the test sensitivity (SE) and specificity (SP) were evaluated by the use of a latent class model. Statistical inference was performed by classical maximum likelihood and by Bayesian techniques. The maximum-likelihood analysis was performed assuming conditional independence of tests, whereas the Bayesian approach allowed for conditional dependence. No clear conclusion could be drawn about conditional dependence of tests. Results with maximum likelihood (under conditional independence) and posterior Bayes (under conditional dependence) were practically the same. Conservative estimates of SE and SP (with 95% confidence intervals) for M108 were 98.6 (96.7; 99.6) and 96.7 (92.9; 98.8) and for S108 99.5 (98.2; 99.9) and 95.4 (90.9; 98.1), respectively. The ELISA 108 using either milk or serum to detect BLV-infected animals had comparable SE and SP with the official AGID and a commercial ELISA test, which are currently the most widely accepted tests for the serological diagnosis of BLV infection. Therefore, ELISA 108 can be used as an alternative test in monitoring and control programs.
Animals↗
Safety related drug-labelling changes: findings from two data mining algorithms.
INTRODUCTION: With increasing volumes of postmarketing safety surveillance data, data mining algorithms (DMAs) have been developed to search large spontaneous reporting system (SRS) databases for disproportional statistical dependencies between drugs and events. A crucial question is the proper deployment of such techniques within the universe of methods historically used for signal detection. One question of interest is comparative performance of algorithms based on simple forms of disproportionality analysis versus those incorporating Bayesian modelling. A potential benefit of Bayesian methods is a reduced volume of signals, including false-positive signals. OBJECTIVE: To compare performance of two well described DMAs (proportional reporting ratios [PRRs] and an empirical Bayesian algorithm known as multi-item gamma Poisson shrinker [MGPS]) using commonly recommended thresholds on a diverse data set of adverse events that triggered drug labelling changes. METHODS: PRRs and MGPS were retrospectively applied to a diverse sample of drug-event combinations (DECs) identified on a government Internet site for a 7-month period. Metrics for this comparative analysis included the number and proportion of these DECs that generated signals of disproportionate reporting with PRRs, MGPS, both or neither method, differential timing of signal generation between the two methods, and clinical nature of events that generated signals with only one, both or neither method. RESULTS: There were 136 relevant DECs that triggered safety-related labelling changes for 39 drugs during a 7-month period. PRRs generated a signal of disproportionate reporting with almost twice as many DECs as MGPS (77 vs 40). No DECs were flagged by MGPS only. PRRs highlighted DECs in advance of MGPS (1-15 years) and a label change (1-30 years). For 59 DECs, there was no signal with either DMA. DECs generating signals of disproportionate reporting with only PRRs were both medically serious and non-serious. DISCUSSION/CONCLUSION: In most instances in which a DEC generated a signal of disproportionate reporting with both DMAs (almost twice as many with PRRs), the signal was generated using PRRs in advance of MGPS. No medically important events were signalled only by MGPS. It is likely that the incremental utility of DMAs are highly situation-dependent. It is clear, however, that the volume of signals generated by itself is an inadequate criterion for comparison and that clinical nature of signalled events and differential timing of signals needs to be considered. Accepting commonly recommended threshold criteria for DMAs examined in this study as universal benchmarks for signal detection is not justified.
Algorithms↗
Comparison of phylogenetic metrics of transmission in symptomatic and asymptomatic tuberculosis.
BACKGROUND: Understanding drivers of Mycobacterium tuberculosis (Mtb) transmission remains a critical challenge in high-burden settings. Tuberculosis control efforts traditionally target symptomatic individuals, yet the role of asymptomatic cases in sustaining transmission is increasing recognized. METHODS: We conducted a genomic and epidemiological analysis of Mtb isolates collected in Mato Grosso do Sul, Brazil, between 2008 and 2024. From 2017 to 2022, active case finding was performed in three of the state's largest prisons, whereby sputum was collected from individuals irrespective of symptoms and tested by GeneXpert and culture. We evaluated several metrics of recent transmission from symptomatic and asymptomatic individuals, including phylogenetic clustering, Time-scaled Haplotype Density (THD), Local Branching Index (LBI), and transmission probabilities inferred using the Bayesian Reconstruction and Evolutionary Analysis of Transmission Histories (BREATH). FINDINGS: We sequenced 2,362 Mtb strains, of which 3.5% (115/2,362) were resistant to at least one drug, and 0.6% (16/2,362) were multi-drug resistant. Most strains were lineage 4, and 78.2% of all isolates were part of a genomic cluster. Among 2,362 individuals with tuberculosis, 1,137 were incarcerated at the time of diagnosis. Among these, 505 were identified through active case finding: 277 had symptomatic disease and 228 had asymptomatic tuberculosis. There was no significant difference in phylogenetic clustering proportion (77% vs. 85%; p= 0.816), THD (median 0.50 vs. 0.39; p = 0.120), or LBI (median 0.00863 vs. 0.00871; p = 0.086) between symptomatic and asymptomatic individuals. Bayesian transmission trees revealed no significant difference in the number of secondary infections inferred from symptomatic compared with asymptomatic individuals (p = 0.56). These findings were consistent across genomic clusters and robust to model assumptions. INTERPRETATION: We identified no differences in transmission from symptomatic compared with asymptomatic individuals, using several genomic measures of transmission, underscoring the substantial contribution that asymptomatic tuberculosis makes to transmission at the population level.
Asymptomatic↗
Implementing the Bayesian paradigm: reporting research results over the World-Wide Web.
For decades, statisticians, philosophers, medical investigators and others interested in data analysis have argued that the Bayesian paradigm is the proper approach for reporting the results of scientific analyses for use by clients and readers. To date, the methods have been too complicated for non-statisticians to use. In this paper we argue that the World-Wide Web provides the perfect environment to put the Bayesian paradigm into practice: the likelihood function of the data is parsimoniously represented on the server side, the reader uses the client to represent her prior belief, and a downloaded program (a Java applet) performs the combination. In our approach, a different applet can be used for each likelihood function, prior belief can be assessed graphically, and calculation results can be reported in a variety of ways. We present a prototype implementation, BayesApplet, for two-arm clinical trials with normally-distributed outcomes, a prominent model for clinical trials. The primary implication of this work is that publishing medical research results on the Web can take a form beyond or different from that currently used on paper, and can have a profound impact on the publication and use of research results.
Bayes Theorem↗
[Maternal and child health indicators in Belo Horizonte, Minas Gerais State, Brazil, 2001: an analysis of intra-urban differences].
Spatial analysis of health indicators is as an important methodology for detection of intra-urban differences. This study aimed to examine the spatial distribution of all live births in Belo Horizonte, analyzing the presence of spatial clusters of health indicators for newborns and their mothers, using data from the Information System on Live Births. For each area covered by a Primary Health Care Unit, we calculated the indicators using empirical Bayesian methods. For spatial analysis, the indicators obtained from the global Moran (I) index and Local Indicators of Spatial Association (LISA) were used. Analysis using LISA showed the presence of relevant spatial clusters for adolescent mothers and those with low schooling, stillbirths in previous pregnancies, cesarean sections, and low attendance at prenatal care, especially in areas with low socio-demographic characteristics. The methodology adopted was configured as a key instrument for detecting risk areas where clustering occurs. The method can easily be incorporated into health surveillance systems as a mechanism for controlling events related to births in a given area.
Adolescent↗
Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.
BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.
Humans↗
Effects of artifact rejection and bayesian weighting on the auditory brainstem response during quiet and active behavioral conditions.
PURPOSE: To evaluate the effects of 2 noise reduction techniques on the auditory brainstem response (ABR). METHOD: ABRs of 20 normal hearing adults were recorded during quiet and active behavioral conditions using 2 stimulus intensity levels. Wave V amplitudes and residual noise root-mean-square values were measured following the offline application of artifact rejection and Bayesian weighting. Repeated measures analysis of variance and Bonferroni adjusted pairwise t tests were utilized to evaluate significant main effects and interactions between the 2 noise reduction techniques. RESULTS: ABRs recorded during the quiet behavioral condition resulted in minimal differences in wave V amplitude and noise reduction improvement, suggesting that the 2 techniques were equally effective under ideal recording situations. During the active behavioral condition, however, the techniques differed significantly in the ability to preserve the evoked potential and reduce noise. Consequently, strict artifact rejection levels resulted in an inherent underestimation of wave V amplitudes when compared with the Bayesian approach. CONCLUSION: Artifact rejection had a detrimental effect on waveform morphology of the ABR. This could lead to difficulty in ABR interpretation when patients are active and ultimately result in diagnostic errors.
Adult↗
Profile and analysis of gene expression changes during early development in germinating spores of Ceratopteris richardii.
Analysis of an expressed sequence tag library with more than 5,000 sequences from spores of the fern Ceratopteris richardii reveals that more than 3,900 of them represent distinct genes, and almost 70% of these have significant similarity to Arabidopsis (Arabidopsis thaliana) genes. Eight genes are common between three very different dormant plant systems, Ceratopteris spores, Arabidopsis seeds, and Arabidopsis pollen. We evaluated the pattern of mRNA abundance over the first 48 h of spore development using a microarray of cDNAs representing 3,207 distinct genes of C. richardii and determined the relative levels of RNA abundance for 3,143 of these genes using a Bayesian method of statistical analysis. More than 900 of them (29%) show a significant change between any of the five time points analyzed, and these have been annotated based on their sequence similarity with the Arabidopsis proteome. Novel data arising from these analyses identify genes likely to be critical for the germination and subsequent early development of diverse cells and tissues emerging from dormancy.
Arabidopsis↗
Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance.
Precise segmentation of microcalcifications is essential in the development of accurate mammographic computer-aided diagnosis (CAD) schemes. We have designed a radial gradient-based segmentation method for microcalcifications, and compared it to both the region-growing segmentation method currently used in our CAD scheme and to the watershed segmentation method. Two observer studies were conducted to subjectively evaluate the proposed segmentation method. The first study (A) required observers to rate the segmentation accuracy on a 100-point scale. The second observer evaluation (B) was a preference study in which observers selected their preferred method from three displayed segmentation methods. In study A, the observers gave an average accuracy rating of 88 for the radial gradient-based and 50 for the region-growing segmentation method. In study B, the two observers selected the proposed method 56% and 62% of the time. We also investigated the effect of the proposed segmentation method on the performance of computerized classification scheme in differentiating malignant from benign clustered microcalcifications. The performances of the classification scheme using a linear discriminant analysis (LDA) or a Bayesian artificial neural network classifier both showed statistically significant improvements when using the proposed segmentation method. The areas under the receiver-operating characteristic curves for case-based performance when using the LDA classifier were 0.86 with the proposed segmentation method, 0.80 with the region-growing method, and 0.83 with the watershed method.
Algorithms↗
Bayesian model selection for genome-wide epistatic quantitative trait loci analysis.
The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.
Algorithms↗
A Bayesian hierarchical model for multi-level repeated ordinal data: analysis of oral practice examinations in a large anaesthesiology training programme.
Oral practice examinations (OPEs) are used in many anaesthesiology programmes to familiarize anaesthesiology residents with the format of the oral examination administered by the American Board of Anesthesiology. The OPE outcome (final grade) consists of 'Definite Not Pass', 'Probable Not Pass', 'Probable Pass' and 'Definite Pass'. In our study to assess the validity of the OPE, residents took an average of two (ranging from one to six) OPEs, each of which was evaluated by two board certified anaesthesiologists randomly selected from a pool of 12. A key question of interest was to identify factors, for example, the length of training, didactic experience and other characteristics, that most influence OPE outcome. In addition, we were interested in assessing the reliability of the final grade, that is, the covariance parameters are of interest as well. However, estimating variance components in multi-level data with an unequal number of repeated ordinal outcomes presents several statistical challenges, such as how to estimate high dimensional random effects parameters, especially for ordinal outcomes. We propose a Bayesian hierarchical proportional odds model for data with such complexity. The flexibility of such a model allows us to make inference on the association of OPE outcomes with other factors and to estimate the variance components as well.
Anesthesiology↗