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PubMed2001

Bayesian segregation analysis of somatic cell scores of Ontario Holstein cattle.

Bayesian segregation analysis using a Gibbs sampling approach was applied to four sets of simulated data and one set of field data to detect evidence of major genes affecting the evaluated trait. The substitution effect of a major gene and its allelic frequency were estimated for each set of data. For two datasets simulated with a model with no major gene effect, the resulting estimates of polygenic variance and heritability agreed with the simulated values and tests for the presence of a major gene were not significant. Analyses of two sets of data simulated with a major gene produced posterior distributions that gave significant evidence of major gene effects but underestimated the substitution values of the major gene. The segregation analysis of field data suggested that a major gene significantly affected somatic cell score (SCS) in the population of Ontario Holstein cattle. The estimated heritability of SCS was approximately 0.16. The major gene variance accounted for about 17% of the total genetic variance and the point estimate of the frequency of the allele having a positive effect on SCS was 0.30. However, the precision of these estimates is questionable based on the simulation results. The effect of the major gene may be underestimated.

Animals↗

PubMed2006

Rating exposure control using Bayesian decision analysis.

A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from the AIHA exposure category scheme and refer to (0) negligible or trivial exposure (i.e., the true X 0.95 < or =1%OEL), (1) highly controlled (i.e., X 0.95 < or =10%OEL), (2) well controlled (i.e., X 0.95 < or =50%OEL), (3) controlled (i.e., X 0.95 < or =100%OEL), or (4) poorly controlled (i.e., X0.95 > or =1%OEL) exposures. Unlike conventional statistical methods applied to exposure data, Bayesian statistical techniques can be adapted to explicitly take into account professional judgment or other sources of information. The analysis output consists of a distribution (i.e., set) of decision probabilities: e.g., 1%, 80%, 12%, 5%, and 2% probability that the exposure profile is a Category 0, 1, 2, 3, or 4 exposure. By inspection of these decision probabilities, rather than the often difficult to interpret point estimates (e.g., the sample 95th percentile exposure) and confidence intervals, a risk manager can be better positioned to arrive at an effective (i.e., correct) and efficient decision. Bayesian decision methods are based on the concepts of prior, likelihood, and posterior distributions of decision probabilities. The prior decision distribution represents what an industrial hygienist knows about this type of operation, using professional judgment; company, industry, or trade organization experience; historical or surrogate exposure data; or exposure modeling predictions. The likelihood decision distribution represents the decision probabilities based on an analysis of only the current data. The posterior decision distribution is derived by mathematically combining the functions underlying the prior and likelihood decision distributions, and represents the final decision probabilities. Advantages of Bayesian decision analysis include: (a) decision probabilities are easier to understand by risk managers and employees; (b) prior data, professional judgment, or modeling information can be objectively incorporated into the decision-making process; (c) decisions can be made with greater certainty; (d) the decision analysis can be constrained to a more realistic "parameter space" (i.e., the range of plausible values for the true geometric mean and geometric standard deviation); and (e) fewer measurements are necessary whenever the prior distribution is well defined and the process is fairly stable. Furthermore, Bayesian decision analysis provides an obvious feedback mechanism that can be used by an industrial hygienist to improve professional judgment. For example, if the likelihood decision distribution is inconsistent with the prior decision distribution then it is likely that either a significant process change has occurred or the industrial hygienist's initial judgment was incorrect. In either case, the industrial hygienist should readjust his judgment regarding this operation.

Bayes Theorem↗

PubMed2006

Phylogeny of sipunculan worms: A combined analysis of four gene regions and morphology.

The intra-phyletic relationships of sipunculan worms were analyzed based on DNA sequence data from four gene regions and 58 morphological characters. Initially we analyzed the data under direct optimization using parsimony as optimality criterion. An implied alignment resulting from the direct optimization analysis was subsequently utilized to perform a Bayesian analysis with mixed models for the different data partitions. For this we applied a doublet model for the stem regions of the 18S rRNA. Both analyses support monophyly of Sipuncula and most of the same clades within the phylum. The analyses differ with respect to the relationships among the major groups but whereas the deep nodes in the direct optimization analysis generally show low jackknife support, they are supported by 100% posterior probability in the Bayesian analysis. Direct optimization has been useful for handling sequences of unequal length and generating conservative phylogenetic hypotheses whereas the Bayesian analysis under mixed models provided high resolution in the basal nodes of the tree.

Animals↗

PubMed2006

In silico identification and Bayesian phylogenetic analysis of multiple new mammalian kallikrein gene families.

Kallikrein gene families have been identified previously in genomes of the human, the mouse, and the rat, and individual kallikrein-like genes have been found in many more species. This study presents the in silico identification of kallikrein gene families in the recently sequenced genomes of four additional mammalian species, the chimpanzee, the dog, the pig, and the opossum. Phylogenies were constructed with gene sequences from all seven mammalian families, using Bayesian analysis, which clarified the evolutionary relationships between these genes. Individual gene sequences, as well as concatenated constructs of multiple sequences, were used. Fifteen kallikrein genes were located in the chimpanzee (Pan troglodytes) genome, while only 14 were identified in the canine (Canis familiaris) genome as no orthologue to human KLK3 was found. Thirteen genes were identified from the pig (Sus scrofa) genome, which lacked homologues to KLK2 and KLK3, and 11 genes, orthologous to human KLK5 through KLK15, were found in the opossum (Monodelphis domestica) genome. No kallikrein genes were identified from the available genome sequences of the chicken (Gallus gallus) or African clawed frog (Xenopus tropicalis). Within the family of kallikreins several subfamilies were suggested by phylogenetic analysis. One consisted of KLK4, KLK5, and KLK14; another of KLK9, KLK11, and KLK15; a third of KLK10 and KLK12; a fourth of KLK6 and KLK13; and finally one of KLK8 and the classical kallikreins (KLK1, KLK2, and KLK3).

Animals↗

PubMed2005

Diversity of bat-associated Leptospira in the Peruvian Amazon inferred by bayesian phylogenetic analysis of 16S ribosomal DNA sequences.

The role of bats as potential sources of transmission to humans or as maintenance hosts of leptospires is poorly understood. We quantified the prevalence of leptospiral colonization in bats in the Peruvian Amazon in the vicinity of Iquitos, an area of high biologic diversity. Of 589 analyzed bats, culture (3 of 589) and molecular evidence (20 of 589) of leptospiral colonization was found in the kidneys, yielding an overall colonization rate of 3.4%. Infection rates differed with habitat and location, and among different bat species. Bayesian analysis was used to infer phylogenic relationships of leptospiral 16S ribosomal DNA sequences. Tree topologies were consistent with groupings based on DNA-DNA hybridization studies. A diverse group of leptospires was found in peri-Iquitos bat populations including Leptospira interrogans (5 clones), L. kirschneri (1), L. borgpetersenii (4), L. fainei (1), and two previously undescribed leptospiral species (8). Although L. kirschenri and L. interrogans have been previously isolated from bats, this report is the first to describe L. borgpetersenii and L. fainei infection of bats. A wild animal reservoir of L. fainei has not been previously described. The detection in bats of the L. interrogans serovar Icterohemorrhagiae, a leptospire typically maintained by peridomestic rats, suggests a rodent-bat infection cycle. Bats in Iquitos maintain a genetically diverse group of leptospires. These results provide a solid basis for pursuing molecular epidemiologic studies of bat-associated Leptospira, a potentially new epidemiologic reservoir of transmission of leptospirosis to humans.

Animals↗

PubMed1990

[Population pharmacokinetic analysis of indocyanine green retention rate at 15 minutes].

Using the population pharmacokinetic (Bayesian) method, we investigated the indocyanine green (ICG) retention rate at 15 minutes (R 15) in patients with liver disease. For the Bayesian analysis, the mean and variance of parameters, the disappearance rate (K) and the distribution volume, were calculated by the one compartment analysis with data of inpatients. The mean value of the disappearance rate (K) was shown to be smaller and the variances of parameters of the patients were larger than normal values reported, so that it was suggested that the studied group included patients with various kinds of liver diseases. Accordingly, the Bayesian analysis was performed using above statistical results. The distribution volumes, calculated from three points measurements by Bayesian method, were estimated smaller and the initial concentrations higher than those obtained by the one compartment analysis. However, the retention rate (R 15) were well correlated the results of one compartment analysis. The Bayesian analysis using one point measurement at 15 minutes, resulted that the values of retention rate (R 15) were shown about 17% lower, but well correlated with the coefficient gamma = +0.9972 to those obtained by usual method. The disappearance rate (K), which calculated simultaneously from the one point analysis, showed the lowest value on liver cirrhosis. So that, the application of Bayesian analysis to the one point measurement of ICG test was useful to clinical evaluation of liver disease.

Bayes Theorem↗

PubMed2001

[Count on your beliefs. Bayes--not the P value--measures credence].

This article (the second of two) describes traditional (frequentistic) statistical analysis in the context of the confidence interval. Bayesian analysis is described in two settings. In the choice between diagnostic alternatives, the bayesian approach offers useful integration of new information with previous knowledge. With regard to the evaluation of clinical trial data, this article exemplifies bayesian analysis as contrasted with traditional analysis, and advantages of the former are cited. A brief literature review exposes the wide applicability of bayesian analysis in medical statistics.

Bayes Theorem↗

PubMed2006

Bayesian population analysis of a harmonized physiologically based pharmacokinetic model of trichloroethylene and its metabolites.

Bayesian population analysis of a harmonized physiologically based pharmacokinetic (PBPK) model for trichloroethylene (TCE) and its metabolites was performed. In the Bayesian framework, prior information about the PBPK model parameters is updated using experimental kinetic data to obtain posterior parameter estimates. Experimental kinetic data measured in mice, rats, and humans were available for this analysis, and the resulting posterior model predictions were in better agreement with the kinetic data than prior model predictions. Uncertainty in the prediction of the kinetics of TCE, trichloroacetic acid (TCA), and trichloroethanol (TCOH) was reduced, while the kinetics of other key metabolites dichloroacetic acid (DCA), chloral hydrate (CHL), and dichlorovinyl mercaptan (DCVSH) remain relatively uncertain due to sparse kinetic data for use in this analysis. To help focus future research to further reduce uncertainty in model predictions, a sensitivity analysis was conducted to help identify the parameters that have the greatest impact on various internal dose metric predictions. For application to a risk assessment for TCE, the model provides accurate estimates of TCE, TCA, and TCOH kinetics. This analysis provides an important step toward estimating uncertainty of dose-response relationships in noncancer and cancer risk assessment, improving the extrapolation of toxic TCE doses from experimental animals to humans.

Animals↗

PubMed1999

Modeling excessive nutrient loading in the environment.

Models addressing excessive nutrient loading in the environment originated over 50 years ago with the simple nutrient concentration thresholds proposed by Sawyer (1947. Fertilization of lakes by agricultural and urban drainage. New Engl. Water Works Assoc. 61, 109-127). Since then, models have improved due to progress in modeling techniques and technology as well as enhancements in scientific knowledge. Several of these advances are examined here. Among the recent approaches in modeling techniques we review are error propagation, model confirmation, generalized sensitivity analysis, and Bayesian analysis. In the scientific arena and process characterization, we focus on advances in surface water modeling, discussing enhanced modeling of organic carbon, improved hydrodynamics, and refined characterization of sediment diagenesis. We conclude with some observations on future needs and anticipated developments.

Journal Article↗

PubMed2005

Typification of cider brandy on the basis of cider used in its manufacture.

A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.

Alcoholic Beverages↗

PubMed2006

A Bayesian approach for the evaluation of six diagnostic assays and the estimation of Cryptosporidium prevalence in dairy calves.

The prevalence of Cryptosporidium in calves and the test properties of six diagnostic assays (microscopy (ME), an immunofluorescence assay (IFA), two ELISA and two PCR assays) were estimated using Bayesian analysis. In a first Bayesian approach, the test results of the four conventional techniques were used: ME, IFA and two ELISA. This four-test approach estimated that the calf prevalence was 17% (95% Probability Interval (PI): 0.1-0.28) and that the specificity estimates of the IFA and ELISA were high compared to ME. A six-test Bayesian model was developed using the test results of the 4 conventional assays and 2 PCR assays, resulting in a higher calf prevalence estimate (58% with a 95% PI: 0.5-0.66) and in a different test evaluation: the sensitivity estimates of the conventional techniques decreased in the six-test approach, due to the inclusion of two PCR assays with a higher sensitivity compared to the conventional techniques. The specificity estimates of these conventional assays were comparable in the four-test and six-test approach. These results both illustrate the potential and the pitfalls of a Bayesian analysis in estimating prevalence and test characteristics, since posterior estimates are variables depending both on the data at hand and prior information included in the analysis. The need for sensitive diagnostic assays in epidemiological studies is demonstrated, especially for the identification of subclinically infected animals since the PCR assays identify these animals with reduced oocyst excretion, which the conventional techniques fail to identify.

Animals↗

PubMed2002

An approximate Bayesian risk-analysis for the gastro-intestinal safety of ibuprofen.

PURPOSE: Although several studies on ibuprofen and its gastro-intestinal (GI) risk have been reported, the dose-response relationship was not clear due to the lack of information regarding high-dose exposure. Analysis using Bayesian methods is appropriate whenever data are sparse, although such methods are not easily implemented. METHODS: A retrospective cohort study to assess this dose-response relationship was carried out using a record linkage database. A Bayesian risk-analysis was conducted using the Bayesian bootstrap approximation. Risks of GI events at different dose levels were compared using the posterior distributions and the number of events predicted to occur in the future was estimated. Risk factors such as age, gender and co-morbidity were adjusted for in the analysis. This approximation was compared with the full Bayesian approach using the usual but more computer-intensive tool of Markov Chain Monte Carlo simulation. RESULTS: There were 1, 5 and 10 complicated GI events during exposure to high, medium and low dose ibuprofen with 0.2, 1.8 and 7.0 thousand person-years (PY) exposure, respectively. After adjusting for other risk factors the relative risks of high versus low and medium versus low doses were 6.3 (95% CI = 0.21, 24.17) and 2.5 (95% CI = 0.71, 5.85), respectively. Using the approximate Bayesian method prediction of the number of events in a population of females aged 50-59 with no previous medical problems with 1000 PY drug exposure showed that the estimated probability of having more than five events was 0.048 for the medium-dose group and 0.14 for the high-dose group. CONCLUSIONS: High dose ibuprofen appears to have a considerably greater risk of having a larger number of adverse GI events than a medium dose. The approximate Bayesian bootstrap method was demonstrated to be a robust and easily implemented alternative to the full Bayesian approach to risk analysis whenever data are sparse.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed1999

Bayesian survival analysis using a MARS model.

A Bayesian multivariate adaptive regression spline fitting approach is used to model univariate and multivariate survival data with censoring. The possible models contain the proportional hazards model as a subclass and automatically detect departures from this. A reversible jump Markov chain Monte Carlo algorithm is described to obtain the estimate of the hazard function as well as the survival curve.

Algorithms↗

PubMed2003

Bayesian hierarchical approach to estimate insulin sensitivity by minimal model.

We adopted Bayesian analysis in combination with hierarchical (population) modelling to estimate simultaneously population and individual insulin sensitivity (SI) and glucose effectiveness (SG) with the minimal model of glucose kinetics using data collected during insulin-modified intravenous glucose tolerance test (IVGTT) and made comparison with the standard non-linear regression analysis. After fasting overnight, subjects with newly presenting Type II diabetes according to World Health Organization criteria (n =65; 53 males, 12 females; age, 54 +/- 9 years; body mass index, 30.4 +/- 5.2 kg/m2; means+/-S.D.) underwent IVGTT consisting of a 0.3 g of glucose bolus/kg of body weight given at time zero for 2 min, followed by 0.05 unit of insulin/kg of body weight at 20 min. Bayesian inference was carried out using vague prior distributions and log-normal distributions to guarantee non-negativity and, thus, physiological plausibility of model parameters and associated credible intervals. Bayesian analysis gave estimates of SI in all subjects. Non-linear regression analysis failed in four cases, where Bayesian analysis-derived SI was located in the lower quartile and was estimated with lower precision. The population means of SI and SG provided by Bayesian analysis and non-linear regression were identical, but the interquartile range given by Bayesian analysis was tighter by approx. 20% for SI and by approx. 15% for SG. Individual insulin sensitivities estimated by the two methods were highly correlated ( rS=0.98; P <0.001). However, the correlation in the lower 20% centile of the insulin-sensitivity range was significantly lower than the correlation in the upper 80% centile ( rS=0.71 compared with rS=0.99; P <0.001). We conclude that the Bayesian hierarchical analysis is an appealing method to estimate SI and SG, as it avoids parameter estimation failures, and should be considered when investigating insulin-resistant subjects.

Adult↗

PubMed2005

Replica-exchange Monte Carlo scheme for bayesian data analysis.

We develop a sampling algorithm to explore the probability densities arising in Bayesian data analysis problems. Our algorithm is a multiparameter generalization of a replica-exchange Monte Carlo scheme. The strategy relies on gradual weighing of experimental data and on Tsallis generalized statistics. We demonstrate the effectiveness of the method on nuclear magnetic resonance data for a folded protein.

Algorithms↗

PubMed1995

Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis.

PURPOSE: To test a neural network in differentiation of benign from malignant solitary pulmonary nodules. MATERIALS AND METHODS: Neural networks were trained and tested on the characteristics of 318 nodules. Predictive accuracy of the network was judged for calibration and discrimination. Network results were compared with those with a simpler Bayesian method. RESULTS: The Brier score was 0.142 (calibration, 0.003; discrimination, 0.139) for the neural network and 0.133 for the Bayesian analysis (calibration, 0.012; discrimination, 0.121). Analysis of the calibration curve revealed no significant difference (P < .05) between the slope (b = 1.09) and the line of identity (b = 1) for the neural network or the Bayesian analysis. The area under the receiver operating characteristic curve was 0.871 for the neural network and 0.894 for the Bayesian analysis (P < .05). There were 23 and 21 false-positive predictions and 18 and six false-negative predictions for the neural network and Bayesian analysis, respectively. CONCLUSION: The Bayesian method was better than the neural network in prediction of probability of malignancy in solitary pulmonary nodules.

Adult↗

PubMed2005

Quantitative trait nucleotide analysis using Bayesian model selection.

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.

Bayes Theorem↗

PubMed2002

An examination of the monophyly of morning glory taxa using Bayesian phylogenetic inference.

The objective of this study was to obtain a quantitative assessment of the monophyly of morning glory taxa, specifically the genus Ipomoea and the tribe Argyreieae. Previous systematic studies of morning glories intimated the paraphyly of Ipomoea by suggesting that the genera within the tribe Argyreieae are derived from within Ipomoea; however, no quantitative estimates of statistical support were developed to address these questions. We applied a Bayesian analysis to provide quantitative estimates of monophyly in an investigation of morning glory relationships using DNA sequence data. We also explored various approaches for examining convergence of the Markov chain Monte Carlo (MCMC) simulation of the Bayesian analysis by running 18 separate analyses varying in length. We found convergence of the important components of the phylogenetic model (the tree with the maximum posterior probability, branch lengths, the parameter values from the DNA substitution model, and the posterior probabilities for clade support) for these data after one million generations of the MCMC simulations. In the process, we identified a run where the parameter values obtained were often outside the range of values obtained from the other runs, suggesting an aberrant result. In addition, we compared the Bayesian method of phylogenetic analysis to maximum likelihood and maximum parsimony. The results from the Bayesian analysis and the maximum likelihood analysis were similar for topology, branch lengths, and parameters of the DNA substitution model. Topologies also were similar in the comparison between the Bayesian analysis and maximum parsimony, although the posterior probabilities and the bootstrap proportions exhibited some striking differences. In a Bayesian analysis of three data sets (ITS sequences, waxy sequences, and ITS + waxy sequences) no supoort for the monophyly of the genus Ipomoea, or for the tribe Argyreieae, was observed, with the estimate of the probability of the monophyly of these taxa being less than 3.4 x 10(-7).

Bayes Theorem↗