Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Bayes factor”

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.

At least 37 records · Page 2Linked to original sources
PubMed1984

Antifungal relative inhibition factors: BAY l-9139, bifonazole, butoconazole, isoconazole, itraconazole (R 51211), oxiconazole, Ro 14-4767/002, sulconazole, terconazole and vibunazole (BAY n-7133) compared in vitro with nine established antifungal agents.

Nine new antifungal agents were tested for their activity in vitro in terms of relative inhibition factors (RIFs) against 26 isolates of Candida species, eight isolates of Aspergillus species and six isolates of dermatophyte fungi. Eight of the new compounds were azole antifungals, the ninth was a phenylmorpholine derivative. Against Candida species, all the novel compounds gave RIFs that were of a similar order to RIFs for established imidazole compounds. Two topical antifungals, butoconazole and terconazole, and two systemic antifungals, itraconazole and vibunazole, gave mean RIFs less than 60% in tests with Candida species, and therefore matched clotrimazole, ketoconazole and tioconazole in terms of RIF. However, none of the new compounds gave RIFs as low as amphotericin B against the Candida isolates. Against Aspergillus isolates, itraconazole, with a mean RIF of 25%, was even more active in vitro than amphotericin B. Vibunazole was as active as ketoconazole against Aspergillus isolates. All the new antifungals except Bay l-9139 gave very low RIFs against dermatophyte isolates, and thus matched established imidazole antifungals for inhibitory effects in vitro. In terms of RIF data, all the nine new compounds tested appear to offer reasonable potential for antifungal chemotherapy in vivo. A similar conclusion would not have been drawn from minimal inhibitory concentration data, which tended to show most of the new antifungals in a very poor light. Tests with amphotericin B, 5-fluorocytosine and ketoconazole showed that RIF can vary substantially with the pH of the test medium. For amphotericin B and ketoconazole the best activity was seen at neutral pH values; for 5-fluorocytosine the greatest inhibitory activity was found at lower pH values.

Antifungal Agents↗
PubMed1989

Diagnosis of acute myocardial infarction from two measurements of creatine kinase isoenzyme MB with use of nonparametric probability estimation.

By using bivariate probability estimation for the diagnosis of acute myocardial infarction (AMI) we show how to overcome the difficulties encountered for patients whose clinical presentation is atypical and those encountered when multiple isoenzyme determinations are treated by univariate methods. We use the values for creatine kinase isoenzyme MB measured at the time of admission and 12 h later to estimate the Bayes factors in favor of AMI. The Bayes factors are compiled into a table that the clinician can use to estimate the posterior probability that a patient has AMI. The table of Bayes factors is based on data for a sample of 802 non-AMI patients and 180 AMI patients. Further to validate the method, we randomly chose 200 of the non-AMI and 50 of the AMI patients as an evaluation sample, then used the remaining 602 non-AMI and 130 AMI patients to recompute the Bayes factors. These Bayes factors were used to find the probability of AMI for each of the 250 patients in the evaluation sample. The method resulted in only one false positive and no false negatives. For the misclassified patient the measurements at admission and 12 h later were 1 and 11 U/L; the posterior odds were 15 to 1 in favor of AMI, but in fact the patient was non-AMI.

Creatine Kinase↗
PubMed2005

Experimental designs for reliable detection of linkage disequilibrium in unstructured random population association studies.

A method is given for design of experiments to detect associations (linkage disequilibrium) in a random population between a marker and a quantitative trait locus (QTL), or gene, with a given strength of evidence, as defined by the Bayes factor. Using a version of the Bayes factor that can be linked to the value of an F-statistic with an existing deterministic power calculation makes it possible to rapidly evaluate a comprehensive range of scenarios, demonstrating the feasibility, or otherwise, of detecting genes of small effect. The Bayes factor is advocated for use in determining optimal strategies for selecting candidate genes for further testing or applications. The prospects for fine-scale mapping of QTL are reevaluated in this framework. We show that large sample sizes are needed to detect small-effect genes with a respectable-sized Bayes factor, and to have good power to detect a QTL allele at low frequency it is necessary to have a marker with similar allele frequency near the gene.

Alleles↗
PubMed2006

Methods to classify familial relationships in the presence of laboratory errors, without parental data.

We consider the problem of accurate classification of family relationship in the presence of laboratory error without parental data. We first propose an adjusted version of the test statistic proposed by Ehm and Wagner based on the summation over a large number of genetics markers. We then propose use of the Bayes factor as a classification rule. We prove theoretically that the Bayes factor is the optimal classification rule in that the total classification error is minimized. We show via simulations that both the adjusted Ehm and Wagner method and Bayes factor classification rule reduce misclassification errors, and that the Bayes factor classification rule is robust against under-estimation or over-estimation of laboratory errors. For monozygotic twins versus dizygotic twins, the correct classification rate of the Bayes rule is over 99%. For full-siblings versus half-siblings, the Bayes factor classification rule generally outperforms Ehm and Wagner's method (in Am J Hum Genet 62:181-188, 1998), especially when full-sibling proportion is high.

Diagnostic Errors↗
PubMed2005

Modeling nucleotide evolution at the mesoscale: the phylogeny of the neotropical pitvipers of the Porthidium group (viperidae: crotalinae).

We analyzed the phylogeny of the Neotropical pitvipers within the Porthidium group (including intra-specific through inter-generic relationships) using 1.4 kb of DNA sequences from two mitochondrial protein-coding genes (ND4 and cyt-b). We investigated how Bayesian Markov chain Monte-Carlo (MCMC) phylogenetic hypotheses based on this 'mesoscale' dataset were affected by analysis under various complex models of nucleotide evolution that partition models across the dataset. We develop an approach, employing three statistics (Akaike weights, Bayes factors, and relative Bayes factors), for examining the performance of complex models in order to identify the best-fit model for data analysis. Our results suggest that: (1) model choice may have important practical effects on phylogenetic conclusions even for mesoscale datasets, (2) the use of a complex partitioned model did not produce widespread increases or decreases in nodal posterior probability support, and (3) most differences in resolution resulting from model choice were concentrated at deeper nodes. Our phylogenetic estimates of relationships among members of the Porthidium group (genera: Atropoides, Cerrophidion, and Porthidium) resolve the monophyly of the three genera. Bayesian MCMC results suggest that Cerrophidion and Porthidium form a clade that is the sister taxon to Atropoides. In addition to resolving the intra-specific relationships among a majority of Porthidium group taxa, our results highlight phylogeographic patterns across Middle and South America and suggest that each of the three genera may harbor undescribed species diversity.

Animals↗
PubMed2003

Bayesian model selection for mixtures of structural equation models with an unknown number of components.

This paper considers mixtures of structural equation models with an unknown number of components. A Bayesian model selection approach is developed based on the Bayes factor. A procedure for computing the Bayes factor is developed via path sampling, which has a number of nice features. The key idea is to construct a continuous path linking the competing models; then the Bayes factor can be estimated efficiently via grids in [0, 1] and simulated observations that are generated by the Gibbs sampler from the posterior distribution. Bayesian estimates of the structural parameters, latent variables, as well as other statistics can be produced as by-products. The properties and merits of the proposed procedure are discussed and illustrated by means of a simulation study and a real example.

Bayes Theorem↗
PubMed2005

Bayesian tests of extra-Binomial variability.

A simple model for extra-Binomial variability is the Beta-Binomial. A complication in testing the Binomial against the Beta-Binomial alternative is that the Binomial lies on the boundary of the Beta-Binomial, which forces modifications to the usual asymptotic arguments. In this paper, we propose a Bayesian test using a pair of approximate Bayes factors, one for the case in which the maximum likelihood estimator (MLE) of the extra-Binomial variability is zero and one for the case in which it is positive. These approximate Bayes factors are easy to compute. We evaluate the operating characteristics of the Bayes factors and find them to be more powerful than the likelihood ratio test. We then apply the method to three data sets, including one in which the issue is whether a logistic regression intercept should be considered a random effect. In each case, our approximate Bayes factors are close to the exact Bayes factors, which may also be computed with additional effort.

Amputation, Surgical↗
PubMed2001

Fate of atmospherically deposited polycyclic aromatic hydrocarbons (PAHs) in Chesapeake Bay.

Factors controlling polycyclic aromatic hydrocarbon (PAH) distributions in southern Chesapeake Bay surface sediments were investigated with samples collected seasonally from five sites. Principal components analysis (PCA) suggests unique sources (combustion vs petroleum) and partitioning differences (volatile vs particle reactive) among PAHs, but a common mode of entry for these contaminants to Chesapeake Bay sediments. The fractional concentrations of all PAHs in Bay sediments, with the exception of perylene, were positively correlated with their atmospheric dry deposition fluxes (Fdry) to the Bay indicating that atmospheric deposition of aerosol-bound PAHs to the watershed controls their influx to Bay sediments. Overall, PAH concentrations in Chesapeake Bay sediments were well explained by a multiple-regression model (r2 = 0.88, p = 0.0001) with Fdry and sediment total organic carbon (TOC) content explaining most of the variance (57% and 43%, respectively). However, for many PAHs gas exchange across the air-water interface is of similar or greater magnitude even when Fdry is scaled to the watershed surface area. The fate of PAHs input to the Bay from gas deposition was determined to be uptake and metabolism within the aquatic food web rather than deposition to sediments.

Aerosols↗
PubMed2005

A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles.

Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The semi-Bayesian approach promoted compares the probability of the observed data under disease hypotheses to the probability of the data under a null hypothesis defined by data at all the markers interrogated in a large study. This method automatically adjusts for the effects of diffuse population stratification. It is claimed that this characterization of the evidence for or against disease models may facilitate more appropriate inductions for large-scale genetic studies. Results include (i) an analytic solution for the population stratification-adjusted Bayes' factor, (ii) the relationship between sample size and Bayes' factors, (iii) an extension to an approximate Bayes' factor calculated across closely-linked sites, and (iv) an extension across multiple studies. Although this paper deals exclusively with genetic studies, it is possible to generalize the approach to treat many different large-scale experiments including studies of gene expression and proteomics.

Journal Article↗
PubMed2003

Testing a molecular clock without an outgroup: derivations of induced priors on branch-length restrictions in a Bayesian framework.

We propose a Bayesian method for testing molecular clock hypotheses for use with aligned sequence data from multiple taxa. Our method utilizes a nonreversible nucleotide substitution model to avoid the necessity of specifying either a known tree relating the taxa or an outgroup for rooting the tree. We employ reversible jump Markov chain Monte Carlo to sample from the posterior distribution of the phylogenetic model parameters and conduct hypothesis testing using Bayes factors, the ratio of the posterior to prior odds of competing models. Here, the Bayes factors reflect the relative support of the sequence data for equal rates of evolutionary change between taxa versus unequal rates, averaged over all possible phylogenetic parameters, including the tree and root position. As the molecular clock model is a restriction of the more general unequal rates model, we use the Savage-Dickey ratio to estimate the Bayes factors. The Savage-Dickey ratio provides a convenient approach to calculating Bayes factors in favor of sharp hypotheses. Critical to calculating the Savage-Dickey ratio is a determination of the prior induced on the modeling restrictions. We demonstrate our method on a well-studied mtDNA sequence data set consisting of nine primates. We find strong support against a global molecular clock, but do find support for a local clock among the anthropoids. We provide mathematical derivations of the induced priors on branch length restrictions assuming equally likely trees. These derivations also have more general applicability to the examination of prior assumptions in Bayesian phylogenetics.

Animals↗
PubMed2004

Modelling risk when binary outcomes are subject to error.

We present methods for binomial regression when the outcome is determined using the results of a single diagnostic test with imperfect sensitivity and specificity. We present our model, illustrate it with the analysis of real data, and provide an example of WinBUGS program code for performing such an analysis. Conditional means priors are used in order to allow for inclusion of prior data and expert opinion in the estimation of odds ratios, probabilities, risk ratios, risk differences, and diagnostic test sensitivity and specificity. A simple method of obtaining Bayes factors for link selection is presented. Methods are illustrated and compared with Bayesian ordinary binary regression using data from a study of the effectiveness of a smoking cessation program among pregnant women. Regression coefficient estimates are shown to change noticeably when expert prior knowledge and imperfect sensitivity and specificity are incorporated into the model.

Adult↗
PubMed2005

Bayesian non-response models for categorical data from small areas: an application to BMD and age.

We provide a Bayesian analysis of data categorized into two levels of age (younger than 50 years, at least 50 years) and three levels of bone mineral density (normal, osteopenia, osteoporosis) for white females at least 20 years old in the third National Health and Nutrition Examination Survey. For the sample, the age of each individual is known, but some individuals did not have their BMD measured. We use two types of models: In the ignorable non-response models the propensity to respond does not depend on BMD and age of an individual, while in the non-ignorable non-response models it does. These are the baseline models which are used to derive all models for testing. Our non-ignorable non-response models are 'close' to the ignorable non-response models, thereby reducing the effects of the assumptions about non-respondents that cannot be tested in non-response models. We have data from 35 counties, small areas, and therefore our models are hierarchical, a feature that allows a 'borrowing of strength' across the counties, and they provide a substantial reduction in variation. The non-ignorable non-response models are generalizations of the ignorable non-response models, and therefore, the non-ignorable non-response models allow broader inference. The joint posterior density of the parameters for each model is complex, and therefore, we fit each model using Markov chain Monte Carlo methods to obtain samples which are used to make inference about BMD and age. For each county we can estimate the proportion of individuals in each BMD and age cell of the categorical table, and we can assess the relation between BMD and age using the Bayes factor. A sensitivity analysis shows that there are differences (typically small) in inference that permits different levels of association between BMD and age. A simulation study shows that there is not much difference between the baseline ignorable and non-ignorable non-response models.

Adult↗
PubMed2005

Introduction to Bayesian methods I: measuring the strength of evidence.

Bayesian inference is a formal method to combine evidence external to a study, represented by a prior probability curve, with the evidence generated by the study, represented by a likelihood function. Because Bayes theorem provides a proper way to measure and to combine study evidence, Bayesian methods can be viewed as a calculus of evidence, not just belief. In this introduction, we explore the properties and consequences of using the Bayesian measure of evidence, the Bayes factor (in its simplest form, the likelihood ratio). The Bayes factor compares the relative support given to two hypotheses by the data, in contrast to the P-value, which is calculated with reference only to the null hypothesis. This comparative property of the Bayes factor, combined with the need to explicitly predefine the alternative hypothesis, produces a different assessment of the strength of evidence against the null hypothesis than does the P-value, and it gives Bayesian procedures attractive frequency properties. However, the most important contribution of Bayesian methods is the way in which they affect both who participates in a scientific dialogue, and what is discussed. With the emphasis moved from "error rates" to evidence, content experts have an opportunity for their input to be meaningfully incorporated, making it easier for regulatory decisions to be made correctly.

Bayes Theorem↗
PubMed2004

Bayesian phylogenetic analysis of combined data.

The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.

Animals↗
PubMed2000

Intrinsic priors for model selection using an encompassing model with applications to censored failure time data.

In Bayesian model selection or testing problems one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. Therefore, Bayes factors and posterior probabilities are not well defined under these noninformative priors, making Bayesian model selection and testing problems impossible. We derive the intrinsic Bayes factor (IBF) of Berger and Pericchi (1996a, 1996b) for the commonly used models in reliability and survival analysis using an encompassing model. We also derive proper intrinsic priors for these models, whose Bayes factors are asymptotically equivalent to the respective IBFs. We demonstrate our results in three examples.

Bayes Theorem↗
PubMed2000

Comparing the performance of two indices for spatial model selection: application to two mortality data.

The statistical analysis of spatially correlated data has become an important scientific research topic lately. The analysis of the mortality or morbidity rates observed at different areas may help to decide if people living in certain locations are considered at higher risk than others. Once the statistical model for the data of interest has been chosen, further effort can be devoted to identifying the areas under higher risks. Many scientists, including statisticians, have tried the conditional autoregressive (CAR) model to describe the spatial autocorrelation among the observed data. This model has greater smoothing effect than the exchangeable models, such as the Poisson gamma model for spatial data. This paper focuses on comparing the two types of models using the index LG, the ratio of local to global variability. Two applications, Taiwan asthma mortality and Scotland lip cancer, are considered and the use of LG is illustrated. The estimated values for both data sets are small, implying a Poisson gamma model may be favoured over the CAR model. We discuss the implications for the two applications respectively. To evaluate the performance of the index LG, we also compute the Bayes factor, a Bayesian model selection criterion, to see which model is preferred for the two applications and simulation data. To derive the value of LG, we estimate its posterior mode based on samples derived from the BUGS program, while for Bayes factor we use the double Laplace-Metropolis method, Schwarz criterion, and a modified harmonic mean for approximations. The results of LG and Bayes factor are consistent. We conclude that LG is fairly accurate as an index for selection between Poisson gamma and CAR model. When easy and fast computation is of concern, we recommend using LG as the first and less costly index.

Asthma↗
PubMed1991

Clinical evaluation of recombinant human factor VIII (BAY w 6240) in the treatment of hemophilia A.

A pilot clinical trial was conducted in five patients with severe hemophilia A to evaluate the safety and efficacy of a recombinant human factor VIII preparation, BAY w 6240 (rFVIII). In a comparative pharmacokinetic study of rFVIII and a plasma-derived factor VIII preparation (pdFVIII), the mean t1/2 values for rFVIII at week 1 and week 13 were 16.8 and 14.4 h, while this value for pdFVIII at week -2 was 16.9 h. There were no statistical differences between these values. The mean in vivo recovery rates of rFVIII were comparable to those of pdFVIII. When rFVIII was administered prophylactically three times a week for 4 weeks, no bleeding episodes were observed. Seventy-four bleeding episodes were assessed during the 6-month treatment period. The efficacy rate of the hemostatic effect was confirmed to be 95.9%. No adverse reactions attributable to rFVIII were observed in a total of 178 infusions. Neither FVIII-inhibitors nor antibodies to foreign proteins were detected. Vital signs and laboratory findings showed no significant changes attributable to rFVIII. These results suggest that rFVIII is safe and efficacious as replacement therapy for hemophilia A.

Adult↗
PubMed2005

Testing equality of two functions using BARS.

This article presents two methods of testing the hypothesis of equality of two functions H(0):f(1)(t)=f(2)(t) for all t, in a generalized non-parametric regression framework using a recently developed generalized non-parametric regression method called Bayesian adaptive regression splines (BARS). Of particular interest is the special case of testing equality of two Poisson process intensity functions lambda(1) (t)=lambda(2) (t), which arises frequently in neurophysiological applications. The first method uses Bayes factors, and the second method uses a modified Hotelling T(2) test. Both methods are applied to the analysis of 347 motor cortical neurons and, for certain choices of test criteria, the two methods lead to the same conclusions for all but 7 neurons. A small simulation study of power indicates that the Bayes factor can be somewhat more powerful in small samples. The T(2)-type test should be useful in screening large number of neurons for condition-related activity, while the Bayes factor will be especially helpful in assessing evidence in favour of H(0).

Animals↗