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PubMed2002

[Cervical cancer in Bas-Rhin: trend and prediction of the incidence in 2014].

BACKGROUND: In the world, the cervix cancer is the second commonest cancer in women. Its incidence is decreasing but it is still too frequent. The aim of this study was to predict the incidence of cervix cancer among women in the Department of Bas-Rhin. METHODS: Incidence data were provided by the Bas-Rhin Tumor Registry. The incidence of in situ tumors and invasive cancers was predicted in 2010-2014 by using an age - period - cohort model and a Bayesian approach. RESULTS: The incidence rates predicted by the model, standardized to the European population, were 99.7 per 10(5)population in 2000-2004 (CI 95%: [82.7-118.5]) and 177.1 per 10(5) population in 2010-2014 (CI 95%: [103.7-288.5]) for in situ and 13.0 per 10(5) population (CI 95%: [9.5-17.2]) in 2000-2004 and 11.1 per 10(5)population (CI 95%: [4.5-22.7]) in 2010-2014 for invasive tumors. CONCLUSIONS: The decrease of invasive tumors is due to screening. The improvement of the quality of the screening and treatment of in situ tumors would allow the number of incident cases of cervix cancer to decrease.

Adult↗
PubMed1998

Prediction of serum vancomycin concentrations using one-, two- and three-compartment models with implemented population pharmacokinetic parameters and with the Bayesian method.

Although previous studies have shown that vancomycin has a complicated pharmacokinetic profile requiring description using a two- or, better, three-compartment model, until recently predictions of serum vancomycin concentrations have been mainly based on one- or two-compartment models using computer software packages. In this study, we have predicted serum vancomycin concentrations in 59 patients using one-, two- and three-compartment models with implemented population pharmacokinetic parameters in the Abbott PKS program and by use of the Bayesian method. The percentage errors of predictions made using the one-compartment model were smaller when either the Bayesian method or implemented population pharmacokinetic parameters were used (medians of -8.61% and -9.49%, respectively). Predictions using the one-compartment model with the Bayesian method were less biased (median of -1.52 microgmL(-1). The best predictions were those made using the three-compartment model with the Bayesian method-they were most accurate (median of 3.40 microgmL(-1) and highly precise (median of 11.53 microg(2)mL(-1)). The results suggest that predictions made using the one-compartment model with implemented population pharmacokinetic parameters are preferable if no samples are available, otherwise predictions made using the three-compartment model with the Bayesian method are preferable. The results also supported our previous argument that the greater the number of compartments involved in individualization, the better the predictions obtained using the Bayesian method.

Adult↗
PubMed2002

Classical and Bayesian inference in neuroimaging: theory.

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.

Algorithms↗
PubMed2003

The Bayesian population approach to physiological toxicokinetic-toxicodynamic models--an example using the MCSim software.

The calibration of physiologically based toxicokinetic models against experimental data encompasses the merging of prior knowledge with information present in the data. This prior knowledge is manifested in the scientific literature and associated with various degrees of uncertainty. The most convenient way to combine these sources of information is via the use of Bayesian statistical methods. Furthermore, toxicokinetic models are subject to both inter- and intra-individual variability. This variability may be handled statistically by the use of a population model. The MCSim software, which is available for free download on the Internet, permits the use of a population model in combination with a Bayesian statistical approach. An example of the use of MCSim in a recent model-based risk assessment of dichloromethane (DCM) is given and discussed.

Animals↗
PubMed2004

Hierarchical Bayesian neural network for gene expression temporal patterns.

There are several important issues to be addressed for gene expression temporal patterns' analysis: first, the correlation structure of multidimensional temporal data; second, the numerous sources of variations with existing high level noise; and last, gene expression mostly involves heterogeneous multiple dynamic patterns. We propose a Hierarchical Bayesian Neural Network model to account for the input correlations of time course gene array data. The variations in absolute gene expression levels and the noise can be estimated with the hierarchical Bayesian setting. The network parameters and the hyperparameters were simultaneously optimized with Monte Carlo Markov Chain simulation. Results show that the proposed model and algorithm can well capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated inputs, the overwhelming interactions, and other complex features typically present in microarray data. We test and demonstrate the proposed models with yeast cell cycle temporal data sets. The model performance of Hierarchical Bayesian Neural Network was compared to other popular machine learning methods such as Nearest Neighbor, Support Vector Machine, and Self Organized Map.

Journal Article↗
PubMed1997

Predictive variable selection for the multivariate linear model.

We develop a predictive Bayesian approach to variable selection in the multivariate linear model. A criterion derived from the Bayesian predictive density is proposed and a calibration is provided for it. Reference and informative priors are discussed, and an automated method that focuses on the response variable is proposed for specifying informative priors for the regression parameters. Relationships between the proposed criterion and other several well-known criteria are examined. Illustrative examples involving real data are given to demonstrate the methodology.

Asbestos↗
PubMed2006

Physiologically-based pharmacokinetic modeling of benzene in humans: a Bayesian approach.

Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically-based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra- and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human-population variability in benzene dosimetry.

Analysis of Variance↗
PubMed1996

Applications of a mixture survival model with covariates to the analysis of a depression prevention trial.

This paper presents a case study of model selection for survival analysis data. We use an approximate Bayesian method for model selection based on assessing the posterior probability of competing models given the data. We introduce the Schwarz criteria, an approximation to the logarithm of the Bayes factor, to provide an indication of evidence in favour of one model compared to another. Specifically, in the context of a depression prevention clinical trial we evaluate the efficacy of treatment in preventing or delaying the time to recurrence of depression, and evaluate how differences in the survival distributions between the two treatment groups depend on explanatory variables of interest. This investigation is based on a mixture survival model that explicitly incorporates the possibility of a surviving fraction.

Antidepressive Agents, Tricyclic↗
PubMed2006

Assessing site-interdependent phylogenetic models of sequence evolution.

In recent works, methods have been proposed for applying phylogenetic models that allow for a general interdependence between the amino acid positions of a protein. As of yet, such models have focused on site interdependencies resulting from sequence-structure compatibility constraints, using simplified structural representations in combination with a set of statistical potentials. This structural compatibility criterion is meant as a proxy for sequence fitness, and the methods developed thus far can incorporate different site-interdependent fitness proxies based on other measurements. However, no methods have been proposed for comparing and evaluating the adequacy of alternative fitness proxies in this context, or for more general comparisons with canonical models of protein evolution. In the present work, we apply Bayesian methods of model selection-based on numerical calculations of marginal likelihoods and posterior predictive checks-to evaluate models encompassing the site-interdependent framework. Our application of these methods indicates that considering site-interdependencies, as done here, leads to an improved model fit for all data sets studied. Yet, we find that the use of pairwise contact potentials alone does not suitably account for across-site rate heterogeneity or amino acid exchange propensities; for such complexities, site-independent treatments are still called for. The most favored models combine the use of statistical potentials with a suitably rich site-independent model. Altogether, the methodology employed here should allow for a more rigorous and systematic exploration of different ways of modeling explicit structural constraints, or any other site-interdependent criterion, while best exploiting the richness of previously proposed models.

Bayes Theorem↗
PubMed2001

Pharmacokinetic and pharmacodynamic modeling of anidulafungin (LY303366): reappraisal of its efficacy in neutropenic animal models of opportunistic mycoses using optimal plasma sampling.

The compartmental pharmacokinetics of anidulafungin (VER-002; formerly LY303366) in plasma were characterized with normal rabbits, and the relationships between drug concentrations and antifungal efficacy were assessed in clinically applicable infection models in persistently neutropenic animals. At intravenous dosages ranging from 0.1 to 20 mg/kg of body weight, anidulafungin demonstrated linear plasma pharmacokinetics that fitted best to a three-compartment open pharmacokinetic model. Following administration over 7 days, the mean (+/- standard error of the mean) peak plasma concentration (C(max)) increased from 0.46 +/- 0.02 microg/ml at 0.1 mg/kg to 63.02 +/- 2.93 microg/ml at 20 mg/kg, and the mean area under the concentration-time curve from 0 h to infinity (AUC(0-infinity)) rose from 0.71 +/- 0.04 to 208.80 +/- 24.21 microg. h/ml. The mean apparent volume of distribution at steady state (V(ss)) ranged from 0.953 +/- 0.05 to 1.636 +/- 0.22 liter/kg (nonsignificant [NS]), and clearance ranged from 0.107 +/- 0.01 to 0.149 +/- 0.00 liter/kg/h (NS). Except for a significant prolongation of the terminal half-life and a trend toward an increased V(ss) at the higher end of the dosage range after multiple doses, no significant differences in pharmacokinetic parameters were noted in comparison to single-dose administration. Concentrations in tissue at trough after multiple dosing (0.1 to 10 mg/kg/day) were highest in lung and liver (0.85 +/- 0.16 to 32.64 +/- 2.03 and 0.32 +/- 0.05 to 43.76 +/- 1.62 microg/g, respectively), followed by spleen and kidney (0.24 +/- 0.65 to 21.74 +/- 1.86 and <0.20 to 16.92 +/- 0.56, respectively). Measurable concentrations in brain tissue were found at dosages of > or =0.5 mg/kg (0.24 +/- 0.02 to 3.90 +/- 0.25). Implementation of optimal plasma sampling in persistently neutropenic rabbit infection models of disseminated candidiasis and pulmonary aspergillosis based on the Bayesian approach and model parameters from normal animals as priors revealed a significantly slower clearance (P < 0.05 for all dosage groups) with a trend toward higher AUC(0-24) values, higher plasma concentrations at the end of the dosing interval, and a smaller volume of distribution (P < 0.05 to 0.193 for the various comparisons among dosage groups). Pharmacodynamic modeling using the residual fungal tissue burden in the main target sites as the primary endpoint and C(max), AUC(0-24), time during the dosing interval of 24 h with plasma drug concentration equaling or exceeding the MIC or the minimum fungicidal concentration for the isolate, and tissue concentrations as pharmacodynamic parameters showed predictable pharmacokinetic-pharmacodynamic relationships in experimental disseminated candidiasis that fitted well with an inhibitory sigmoid maximum effect pharmacodynamic model (r(2), 0.492 to 0.819). However, no concentration-effect relationships were observed in experimental pulmonary aspergillosis using the residual fungal burden in lung tissue and survival as parameters of antifungal efficacy. Implementation of optimal plasma sampling in discriminative animal models of invasive fungal infections and pharmacodynamic modeling is a novel approach that holds promise of improving and accelerating our understanding of the action of antifungal compounds in vivo.

Anidulafungin↗
PubMed2004

Using Biowin, Bayes, and batteries to predict ready biodegradability.

Whether or not a given chemical substance is readily biodegradable is an important piece of information in risk screening for both new and existing chemicals. Despite the relatively low cost of Organization for Economic Cooperation and Development tests, data are often unavailable and biodegradability must be estimated. In this paper, we focus on the predictive value of selected Biowin models and model batteries using Bayesian analysis. Posterior probabilities, calculated based on performance with the model training sets using Bayes' theorem, were closely matched by actual performance with an expanded set of 374 premanufacture notice (PMN) substances. Further analysis suggested that a simple battery consisting of Biowin3 (survey ultimate biodegradation model) and Biowin5 (Ministry of International Trade and Industry [MITI] linear model) would have enhanced predictive power in comparison to individual models. Application of the battery to PMN substances showed that performance matched expectation. This approach significantly reduced both false positives for ready biodegradability and the overall misclassification rate. Similar results were obtained for a set of 63 pharmaceuticals using a battery consisting of Biowin3 and Biowin6 (MITI nonlinear model). Biodegradation data for PMNs tested in multiple ready tests or both inherent and ready biodegradation tests yielded additional insights that may be useful in risk screening.

Bayes Theorem↗
PubMed2006

Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor.

Bayesian-regularized genetic neural networks (BRGNNs) were used to model the binding affinity (IC(50)) for 128 non-peptide antagonists for the human luteinizing hormone-releasing hormone (LHRH) receptor using 2D spatial autocorrelation vectors. As a preliminary step, a linear dependence was established by multiple linear regression (MLR) approach, selecting the relevant descriptors by genetic algorithm (GA) feature selection. The linear model showed to fit the training set (N=102) with R(2)=0.746, meanwhile BRGNN exhibited a higher value of R(2)=0.871. Beyond the improvement of training set fitting, the BRGNN model overcame the linear one by being able to describe 85% of test set (N=26) variance in comparison with 73% the MLR model. Our non-linear QSAR model illustrates the importance of an adequate distribution of atomic properties represented in topological frames and reveals the electronegativities, masses and polarizabilities as the most influencing atomic properties in the structures of the heterocycles under analysis for having an appropriate LHRH antagonistic activity. Furthermore, the ability of the non-linear selected variables for differentiating the data was evidenced when total data set was well distributed in a Kohonen self-organizing map (SOM).

Algorithms↗
PubMed2003

Bayesian estimation of transcript levels using a general model of array measurement noise.

Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.

Bayes Theorem↗
PubMed2002

Bayesian network approach to cell signaling pathway modeling.

The modeling of cellular signaling pathways is an emerging field. Sachs et al. illustrate the application of Bayesian networks to an example cellular pathway involving the activation of focal adhesion kinase (FAK) and extracellular signal-regulated kinase (ERK) in response to fibronectin binding to an integrin. They describe how to use the analysis to select from among proposed models, formulate hypotheses regarding component interactions, and uncover potential dynamic changes in the interactions between these components. Although the data sets currently available for this example problem are too small to definitively point to a particular model, the approach and results provide a glimpse into the power that these methods will achieve once the technology for obtaining the necessary data becomes readily available.

Bayes Theorem↗
PubMed1993

Improved frequency resolution in multidimensional constant-time experiments by multidimensional Bayesian analysis.

The resolution of spectral frequencies in NMR data obtained from discrete Fourier transformation (DFT) along D constant-time dimensions can be improved significantly through extrapolation of the D-dimensional free induction decay (FID) by multidimensional Bayesian analysis. Starting from Bayesian probability theory for parameter estimation and model detection of one-dimensional time-domain data [Bretthorst, (1990) J. Magn. Reson., 88, 533-551; 552-570; 571-595], a theory for the D-dimensional case has been developed and implemented in an algorithm called BAMBAM (BAyesian Model Building Algorithm in Multidimensions). BAMBAM finds the most probable sinusoidal model to account for the systematic portion of any D-dimensional stationary FID. According to the parameters estimated by the algorithm, the FID is extrapolated in D dimensions prior to apodization and Fourier transformation. Multidimensional Bayesian analysis allows for the detection of signals not resolved by the DFT alone or even by sequential one-dimensional extrapolation from mirror-image linear prediction prior to the DFT. The procedure has been tested with a theoretical two-dimensional dataset and with four-dimensional HN(CO)CAHA (Kay et al. (1992) J. Magn. Reson., 98, 443-450) data from a small protein (8 kDa) where BAMBAM was applied to the 13C alpha and H alpha constant-time dimensions.

Algorithms↗
PubMed2006

Dynamic causal modeling of evoked responses in EEG and MEG.

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.

Auditory Pathways↗
PubMed2004

Smoothing a discrete hazard function for the number of patients colonized with Methicillin-resistant Staphylococcus Aureus in an intensive care unit.

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Bayes Theorem↗
PubMed2006

Modeling the frequency and duration of microbial contamination events.

The frequency and duration of microbial contamination events in the environment in which ready-to-eat (RTE) foods are exposed for processing and packaging is subject to uncertainty and variability. Variability, within-model parameter uncertainty, and uncertainty regarding model selection are formally considered in modeling the frequency and duration of such contamination events by Listeria species. The estimated duration of contamination events represents a case where variability dominates with relatively little uncertainty about parameter values or model form. The estimated frequency of contamination events represents a case where there is not only substantial variability but also considerable within-model parameter uncertainty, as well as some uncertainty regarding model selection. The Bayesian Information Criterion provides a formal way of taking into account model uncertainty.

Animals↗