Statistical analysis of Fisher et al. PBPK model of trichloroethylene kinetics.
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Two physiologically based pharmacokinetic models for trichloroethylene (TCE) in mice and humans were calibrated with new toxicokinetic data sets. Calibration is an important step in model development, essential to a legitimate use of models for research or regulatory purposes. A Bayesian statistical framework was used to combine prior information about the model parameters with the data likelihood to yield posterior parameter distributions. For mice, these distributions represent uncertainty. For humans, the use of a population statistical model yielded estimates of both variability and uncertainty in human toxicokinetics of TCE. After adjustment of the models by Markov chain Monte Carlo sampling, the mouse model agreed with a large part of the data. Yet, some data on secondary metabolites were not fit well. The posterior parameter distributions obtained for mice were quite narrow (coefficient of variation [CV] of about 10 or 20%), but these CVs might be underestimated because of the incomplete fit of the model. The data fit, for humans, was better than for mice. Yet, some improvement of the model is needed to correctly describe trichloroethanol concentrations over long time periods. Posterior uncertainties about the population means corresponded to 10-20% CV. In terms of human population variability, volumes and flows varied across subject by approximately 20% CV. The variability was somewhat higher for partition coefficients (between 30 and 40%) and much higher for the metabolic parameters (standard deviations representing about a factor of 2). Finally, the analysis points to differences between human males and females in the toxicokinetics of TCE. The significance of these differences in terms of risk remains to be investigated. (+info)
Variation in the interaction between familial and reproductive factors on the risk of breast cancer according to age, menopausal status, and degree of familiality.
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BACKGROUND: Studies have found that reproductive factors might have a variable effect on the occurrence of breast cancer (BC) according to the existence or not of a family history of BC. The effect of a family history of BC on the risk of BC may also vary according to the age at diagnosis and the degree of kinship. This may confound the relation between familial risk and reproductive factors. A combined analysis was performed to study the interaction between familial risk and reproductive factors according to degree of familiality, age at interview and menopausal status. METHODS: The present analysis included 2948 cases and 4170 controls in seven case-control studies from four countries. The combined relative risks were estimated using a Bayesian random-effects logistic regression model. RESULTS: The main effects of reproductive life factors on the risk of BC are in agreement with previous studies. Two-way interactions between subject's age or menopausal status and a family history of BC were not significant. Although the three-way interaction between age, familial risk and parity was not significant, familial risk seemed to be increased slightly for women with high parity compared with women with low parity in the older age group, and seemed to be slightly decreased for women with high parity compared with women with low parity in younger women. The subject's age also appeared to have an effect on the interaction between familial risk and the age at first childbirth (P = 0.1). CONCLUSIONS: A possible influence of reproductive and menstrual factors on familial risk of BC has been suggested previously and was also evident in the present study. Three-way interactions between age, family history and parity or age at first childbirth might exist and they merit further investigation. (+info)
Bayesian communication: a clinically significant paradigm for electronic publication.
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OBJECTIVE: To develop a model for Bayesian communication to enable readers to make reported data more relevant by including their prior knowledge and values. BACKGROUND: To change their practice, clinicians need good evidence, yet they also need to make new technology applicable to their local knowledge and circumstances. Availability of the Web has the potential for greatly affecting the scientific communication process between research and clinician. Going beyond format changes and hyperlinking, Bayesian communication enables readers to make reported data more relevant by including their prior knowledge and values. This paper addresses the needs and implications for Bayesian communication. FORMULATION: Literature review and development of specifications from readers', authors', publishers', and computers' perspectives consistent with formal requirements for Bayesian reasoning. RESULTS: Seventeen specifications were developed, which included eight for readers (express prior knowledge, view effect size and variability, express threshold, make inferences, view explanation, evaluate study and statistical quality, synthesize multiple studies, and view prior beliefs of the community), three for authors (protect the author's investment, publish enough information, make authoring easy), three for publishers (limit liability, scale up, and establish a business model), and two for computers (incorporate into reading process, use familiar interface metaphors). A sample client-only prototype is available at http://omie.med.jhmi.edu/bayes. CONCLUSION: Bayesian communication has formal justification consistent with the needs of readers and can best be implemented in an online environment. Much research must be done to establish whether the formalism and the reality of readers' needs can meet. (+info)
Bayesian fine-scale mapping of disease loci, by hidden Markov models.
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We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers-in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain-Monte Carlo simulation methods, we obtain posterior distributions of model parameter estimates including disease-gene location and the age of the disease-predisposing mutation. In addition, we allow for heterogeneity in recombination rates, across the candidate region, to account for recombination hot and cold spots. We also obtain, for the ancestral marker haplotype, a posterior distribution that is unique to our method and that, unlike maximum-likelihood estimation, can properly account for uncertainty. We apply the method to data for cystic fibrosis and Huntington disease, for which mutations in disease genes have already been identified. The new method performs well compared with existing multi-locus mapping methods. (+info)
Modeling splice sites with Bayes networks.
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MOTIVATION: The main goal in this paper is to develop accurate probabilistic models for important functional regions in DNA sequences (e.g. splice junctions that signal the beginning and end of transcription in human DNA). These methods can subsequently be utilized to improve the performance of gene-finding systems. The models built here attempt to model long-distance dependencies between non-adjacent bases. RESULTS: An efficient modeling method is described which models biological data more accurately than a first-order Markov model without increasing the number of parameters. Intuitively, a small number of parameters helps a learning system to avoid overfitting. Several experiments with the model are presented, which show a small improvement in the average accuracy as compared with a simple Markov model. These experiments suggest that single long distance dependencies do not help the recognition problem, thus confirming several previous studies which have used more heuristic modeling techniques. AVAILABILITY: This software is available for downloaded and as a web resource at http://www.ai.uic.edu/software CONTACT: [email protected] (+info)
Accommodating phylogenetic uncertainty in evolutionary studies.
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Many evolutionary studies use comparisons across species to detect evidence of natural selection and to examine the rate of character evolution. Statistical analyses in these studies are usually performed by means of a species phylogeny to accommodate the effects of shared evolutionary history. The phylogeny is usually treated as known without error; this assumption is problematic because inferred phylogenies are subject to both stochastic and systematic errors. We describe methods for accommodating phylogenetic uncertainty in evolutionary studies by means of Bayesian inference. The methods are computationally intensive but general enough to be applied in most comparative evolutionary studies. (+info)
A Bayesian model of temporal frequency masking.
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The data of Anderson and Burr [1985. Vision Research, 25, 1147-1154] on the temporal-frequency (TF) specificity of noise maskers indicate that the effect of TF masking is broad and varies across spatial frequency (SF) channels. One subtle but significant feature of the data is that the TF at which the effect of masking is maximal falls continuously as the test TF falls. This continuous shift is hard to reconcile with models of detection in the literature that relate detection to the most sensitive filter, without resorting to a large number of temporal filters. We developed a new model, which relies on only three temporal filters and posits that detection is the result of a threshold decision based on the compound Bayesian probability of all filter responses, not just the most sensitive filter. (+info)
Bayesian mapping of quantitative trait loci for complex binary traits.
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A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request. (+info)