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At least 1,531 records · Page 85Linked to original sources
PubMed2002

Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force.

PURPOSE: To synthesize new data on breast cancer screening for the U.S. Preventive Services Task Force. DATA SOURCES: MEDLINE; the Cochrane Controlled Trials Registry; and reference lists of reviews, editorials, and original studies. STUDY SELECTION: Eight randomized, controlled trials of mammography and 2 trials evaluating breast self-examination were included. One hundred fifty-four publications of the results of these trials, as well as selected articles about the test characteristics and harms associated with screening, were examined. DATA EXTRACTION: Predefined criteria were used to assess the quality of each study. Meta-analyses using a Bayesian random-effects model were conducted to provide summary relative risk estimates and credible intervals (CrIs) for the effectiveness of screening with mammography in reducing death from breast cancer. DATA SYNTHESIS: For studies of fair quality or better, the summary relative risk was 0.84 (95% CrI, 0.77 to 0.91) and the number needed to screen to prevent one death from breast cancer after approximately 14 years of observation was 1224 (CrI, 665 to 2564). Among women younger than 50 years of age, the summary relative risk associated with mammography was 0.85 (CrI, 0.73 to 0.99) and the number needed to screen to prevent one death from breast cancer after 14 years of observation was 1792 (CrI, 764 to 10 540). For clinical breast examination and breast self-examination, evidence from randomized trials is inconclusive. CONCLUSIONS: In the randomized, controlled trials, mammography reduced breast cancer mortality rates among women 40 to 74 years of age. Greater absolute risk reduction was seen among older women. Because these results incorporate several rounds of screening, the actual number of mammograms needed to prevent one death from breast cancer is higher. In addition, each screening has associated risks and costs.

Adult↗
PubMed2003

Spatiotemporal oviposition and habitat preferences of Ochlerotatus triseriatus and Aedes albopictus in an emerging focus of La Crosse virus.

The number of cases of encephalitis caused by La Crosse virus recently has increased in southwestern Virginia counties. This article presents results of a study conducted from May to September 2000 in Wise County, VA, that examined the area-wide oviposition activity and habitat preferences of Ochlerotatus triseriatus and Aedes albopictus, potential vectors of La Crosse virus in the region. Data from 490 ovitrap collections made throughout the county showed that mean oviposition activity throughout the study was higher for Oc. triseriatus (20.4 eggs/trap-day) than for Ae. albopictus (3.7 eggs/trap-day). The 2 species also had distinct habitat preferences for oviposition, with Oc. triseriatus favoring forested habitats and Ae. albopictus favoring urban/residential habitats. A landcover map of 6 habitat types derived from Landsat satellite imagery of the county showed that 63% of the county was forested and 18% was urban/residential. A Bayesian decision-rule model that incorporated the ovitrap data and landcover map was moderately successful at predicting the occurrence of high oviposition activity and abundance of the 2 species. The predictions reflected seasonal and spatial fluctuations in oviposition activity, with accuracies between 55 and 79% for Oc. triseriatus and 70 and 94% for Ae. albopictus. Kappa (K), a measure of the predictive power of the model, varied from poor (K < 0.4) to good (0.4 < K < 0.75) for both species, and was highest during periods when actual egg abundance was high. This suggests that the predictions were most accurate during periods when the risk for La Crosse virus transmission is greatest. Limitations and suggestions for improving the model are discussed.

Aedes↗
PubMed1994

Mixed Bayesian networks: a mixture of Gaussian distributions.

Mixed Bayesian networks are probabilistic models associated with a graphical representation, where the graph is directed and the random variables are discrete or continuous. We propose a comprehensive method for estimating the density functions of continuous variables, using a graph structure and a set of samples. The principle of the method is to learn the shape of densities from a sample of continuous variables. The densities are approximated by a mixture of Gaussian distributions. The estimation algorithm is a stochastic version of the Expectation Maximization algorithm (Stochastic EM algorithm). The inference algorithm corresponding to our model is a variant of junction three method, adapted to our specific case. The approach is illustrated by a simulated example from the domain of pharmacokinetics. Tests show that the true distributions seem sufficiently fitted for practical application.

Algorithms↗
PubMed1996

[Method and application limitations of decision support systems in acute pancreatitis].

Several decision support systems (DSSs) for acute pancreatitis (AP) were analyzed with reference to development methods, procedure limits and operational performances. Almost all the DSSs have been addressed to the early definition of severity, which appears the only decisional point in the approach to the management of AP. None of the three groups of methods, multifactor, scoring and bayesian systems, provides an explicit evidence of effectiveness. The multifactor systems (Ranson and successive) show inadequacy of design and operational limits which involve poor reliability and conflicting indications from the different centers. The scoring systems (APACHE, SAPS) have been projected and developed for clinical situations quite different from the AP at the onset, and seem more properly to be applicable to the monitoring of its complications. The bayesian systems, although the models used until now present important methodological shortcomings, are those which furnished the best results but are lacking in clinical validation and present a form hardly accepted by the clinician. Despite the disappointing operative results and their limited use in the AP, the DSSs probably constitute one of the most effective tools to improve the management of the severe forms, on condition that the methodology of design enad trial is correctly adjusted.

Acute Disease↗
PubMed1998

Influence of biological variables upon pharmacokinetic parameters of intramuscular methotrexate in rheumatoid arthritis.

The pharmacokinetics of methotrexate were studied in 22 patients receiving 5-15 mg per week in a single i.m. administration for rheumatoid arthritis. The data consisted of 3 plasma levels per patient, taken at 2, 6, and 12 hours after the administration. The concentration of methotrexate was determined by fluorescence polarization immunoassay. The pharmacokinetic parameters of a 2-compartment model were determined by Bayesian estimation using the population values of Bressolle et al. [1996]. The fitted parameters were: total plasma clearance of methotrexate (CL), first-order absorption constant (ka), volume of central compartment (V1), and transfer constants between the 2 compartments (k12 and k21). Additional parameters were derived from the fitted ones: maximal concentration (Cmax), time to maximum (tmax), volume of distribution at steady-state (Vss), and terminal half-life (t1/2). Twenty-one biological covariates were considered to explain the interpatient variability. The relationships between these covariates and the pharmacokinetic parameters were investigated by principal component analysis and multiple regression analysis. About 90% of the variability of CL were explained by 4 variables (sex, age, height and serum creatinine). About 50%-70% of the variability of the other pharmacokinetic parameters were explained by a set of covariates including age, height, creatinine, creatinine clearance, and dose. The effect of dose was noticed mainly on k12, Vss, and t1/2, thus suggesting that the transfer of the drug from plasma to tissues may be nonlinear. The possibility of predicting CL with a good precision would facilitate the computation of dosage regimens in these patients.

Adult↗
PubMed2006

An application of a two-level non-Gaussian state-space model in the analysis of longitudinal papilloma count data.

In this study, a dynamic Bayesian two-level non-Gaussian state-space model is applied in the statistical analysis of longitudinal detectable papilloma count data. This two-level model is established on the basis of the state and hyper-state parameters that depend on the model parameters of biological significance, namely, the initiation rate of normal cells and the birth- and death-rates of initiated cells. As the time-dependent model parameters fluctuate dynamically and stochastically over time, so will the state and hyper-state parameters; thus, smoothness priors that allow a wide range of shapes with no specific forms required on the state/hyper-state parameters can be employed in this model. Gibbs sampler, a Markov chain Monte Carlo approach, is used to implement the Bayesian inferences on the state as well as the hyper-state parameters; and the estimates of the model parameters can be obtained thereon. Illustrations of the Bayesian inference procedure are given by using the datasets from a simulation study as well as a laboratory experiment by Brook et al. [E.A. Brooks, C.M. Kohn, P.J.M. van Birgelen, G.W. Lucier, C.J. Portier, Stochastic models for papilloma formation following exposure to TCDD, Organohalogen Compd. 41 (1999) 521]. Comparing to the parametric approach that assigns specific forms on the time-dependent model parameters, different conclusions are drawn by the two-level state-space modeling approach considered in this study.

Animals↗
PubMed1999

Timing of human immunodeficiency virus type 1 (HIV-1) transmission from mother to child: bayesian estimation using a mixture.

The timing of mother-to-child HIV transmission is not directly observable but influences the infected child's viral and immune status in the neonatal period. A hierarchical model was developed in a Bayesian framework to 'back-calculate' the timing of HIV-1 transmission from mother to child from the virological and immunological kinetics in the infected infant. Joint evolution of viral markers and immune response was modelled as a continuous time Markov process. The modelling of the period from infection to birth was based on a mixture of three distributions taking into account the various mother-to-child transmission pathways: In utero (early or late in gestation) and intrapartum (during the delivery process), integrating the fact that transmission is a continuum during the pregnancy. Gibbs sampling was used to estimate the marginal posterior distributions of the transition intensities between stages of HIV infection and those of the individual times from infection to birth. We applied our model to data on 135 perinatally HIV-1-infected children included in the French Prospective Study on Pediatric HIV infection. The model suggested that transmission occurred late in utero during the last month of pregnancy and that the day of delivery was a particularly critical time in HIV-1 transmission from mother to child. The paper ends with a discussion of model assumptions and a comparison with results obtained using a non-parametric method.

Antibodies, Viral↗
PubMed1987

A Bayesian approach to the multiplicity problem for significance testing with binomial data.

Statistical analyses of simple tumor rates from an animal experiment with one control and one treated group typically consist of hypothesis testing of many 2 X 2 tables, one for each tumor type or site. The multiplicity of significance tests may cause excessive overall false-positive rates. This paper presents a Bayesian approach to the problem of multiple significance testing. We develop a normal logistic model that accommodates the incidences of all tumor types or sites observed in the current experiment simultaneously as well as their historical control incidences. Exchangeable normal priors are assumed for certain linear terms in the model. Posterior means, standard deviations, and Bayesian P-values are computed for an average treatment effect as well as for the effects on individual tumor types or sites. Model assumptions are checked using probability plots and the sensitivity of the parameter estimates to alternative priors is studied. The method is illustrated using tumor data from a chronic animal experiment.

Analysis of Variance↗
PubMedDate not supplied

A classification of disease mapping methods.

This paper considers the underlying principles of depicting disease incidence on geographical maps and uses them to attempt a comparative classification of methods. After a discussion of the possibilities for incorporating time, we consider projection methods, some of which have been used to portray information in a manner supposed to be independent of population density. We then distinguish between non-parametric and model-based methods, including models for areal data using Bayesian ideas. Data in point form are also discussed and it is argued that the relative risk function provides a fundamental model useful for assessing different methods as a whole, some of which are known to be flawed and many of which are untested as regards their statistical properties.

Bayes Theorem↗
PubMed2004

RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees.

MOTIVATION: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods such as parsimony or neighbor joining. Due to the combinatorial and computational complexity the size of trees which can be computed on a Biologist's PC workstation within reasonable time is limited to trees containing approximately 100 taxa. RESULTS: In this paper we present the latest release of our program RAxML-III for rapid maximum likelihood-based inference of large evolutionary trees which allows for computation of 1.000-taxon trees in less than 24 hours on a single PC processor. We compare RAxML-III to the currently fastest implementations for maximum likelihood and bayesian inference: PHYML and MrBayes. Whereas RAxML-III performs worse than PHYML and MrBayes on synthetic data it clearly outperforms both programs on all real data alignments used in terms of speed and final likelihood values. Availability SUPPLEMENTARY INFORMATION: RAxML-III including all alignments and final trees mentioned in this paper is freely available as open source code at http://wwwbode.cs.tum/~stamatak CONTACT: stamatak@cs.tum.edu.

Algorithms↗
PubMed1994

Designing an optimal experiment for Bayesian estimation: application to the kinetics of iodine thyroid uptake.

We consider the problem of designing an optimal experiment for Bayesian estimation of the parameters of a non-linear model. When their distribution is known, the Bayesian approach allows individual estimation from a small number of measurements; the design determines the accuracy of the estimates. We propose to optimize this design by maximizing a general criterion: the expectation of the information supplied by the experiment. This approach is applied to optimize the two sampling times for Bayesian estimation of the kinetics of radioiodine thyroid uptake from an estimated non-parametric prior distribution.

Bayes Theorem↗
PubMed2003

A multi-agent intelligent environment for medical knowledge.

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).

Artificial Intelligence↗
PubMed2000

Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component model.

Variance component analysis of quantitative trait loci (QTL) is an important strategy of genetic mapping for complex traits in humans. The method is robust because it can handle an arbitrary number of alleles with arbitrary modes of gene actions. The variance component method is usually implemented using the proportion of alleles with identity-by-descent (IBD) shared by relatives. As a result, information about marker linkage phases in the parents is not required. The method has been studied extensively under either the maximum-likelihood framework or the sib-pair regression paradigm. However, virtually all investigations are limited to normally distributed traits under a single QTL model. In this study, we develop a Bayes method to map multiple QTL. We also extend the Bayesian mapping procedure to identify QTL responsible for the variation of complex binary diseases in humans under a threshold model. The method can also treat the number of QTL as a parameter and infer its posterior distribution. We use the reversible jump Markov chain Monte Carlo method to infer the posterior distributions of parameters of interest. The Bayesian mapping procedure ends with an estimation of the joint posterior distribution of the number of QTL and the locations and variances of the identified QTL. Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families.

Alleles↗
PubMedDate not supplied

Bayesian approaches to meta-analysis of ROC curves.

A comparative review of important classic and Bayesian approaches to fixed-effects and random-effects meta-analysis of binormal ROC curves and areas underneath them is presented. The ROC analyses results of seven evaluation studies concerning the dexamethasone suppression test provide the basis for a worked example. Particular attention is given to fully Bayesian inference, a novelty in the ROC context, based on Gibbs samples from posterior distributions of hierarchical model parameters and related quantities. Fully Bayesian meta-analysis may properly account for the uncertainty associated with the model parameters, possibly incorporating prior knowledge and beliefs, and allows clinically intuitive predictions of unobserved study effects via calculation of posterior predictive densities. The effects of various different prior specifications (six noninformative as well as one informative) on the posterior estimates are investigated (sensitivity-analysis). Recommendations and suggestions for further research are made. Computer code for the more advanced methods may either be downloaded via the Internet or be found elsewhere.

Bayes Theorem↗
PubMed2002

Scaling to account for heterogeneous variances in a Bayesian analysis of broiler quantitative trait loci.

A Bayesian method for QTL analysis that is capable of accounting for heterogeneity of variance between sexes, is introduced. The Bayesian method uses a parsimonious model that includes scaling parameters for polygenic and QTL allelic effects per sex. Furthermore, the method employs a reduced animal model to increase computational efficiency. Markov Chain Monte Carlo techniques were applied to obtain estimates of genetic parameters. In comparison with previous regression analyses, the Bayesian method 1) estimates dispersion parameters and polygenic effects, 2) uses individual observations instead of offspring averages, and 3) estimates fixed effect levels and covariates and heterogeneity of variance between sexes simultaneously with other parameters, taking uncertainties fully into account. Broiler data collected in a feed efficiency and a carcass experiment were used to illustrate QTL analysis based on the Bayesian method. The experiments were conducted in a population consisting of 10 full-sib families of a cross between two broiler lines. Microsatellite genotypes were determined on generation 1 and 2 animals and phenotypes were collected on third-generation offspring from mating members from different families. Chromosomal regions that seemed to contain a QTL in previous regression analyses and showed heterogeneity of variance were chosen. Traits analyzed in the feed efficiency experiment were BW at 48 d and growth, feed intake, and feed intake corrected for BW between 23 and 48 d. In the carcass experiment, carcass percentage was analyzed. The Bayesian method was successful in finding QTL in all regions previously detected.

Animals↗
PubMed2005

Consideration of RNA secondary structure significantly improves likelihood-based estimates of phylogeny: examples from the bilateria.

Sequences from ribosomal RNA (rRNA) genes have made a huge contribution to our current understanding of metazoan phylogeny and indeed the phylogeny of all of life. That said, some parts of this rRNA-based phylogeny remain unresolved. One approach to increase the resolution of these trees would be to use more appropriate models of sequence evolution in phylogenetic analysis. RNAs transcribed from rRNA genes have a complex secondary structure mediated by base pairing between sometimes distant regions of the rRNA molecule. The pairing between the stem nucleotides has important consequences for their evolution which differs from that of unpaired loop nucleotides. These differences in evolution should ideally be accounted for when using rRNA sequences for phylogeny estimation. We use a novel permutation approach to demonstrate the significant superiority of models of sequence evolution that allow stem and loop regions to evolve according to separate models and, in common with previous studies, we show that 16-state models that take base pairing of stems into account are significantly better than simpler, 4-state, single-nucleotide models. One of these 16-state models has been applied to the phylogeny of the Bilateria using small subunit rRNA (SSU) sequences. Our optimal tree largely echoes previous results based on SSU in particular supporting the tripartite Bilaterian tree of deuterostomes, lophotrochozoans, and ecdysozoans. There are also a number of differences, however, perhaps most important of which is the observation of a clade consisting of the gastrotrichs plus platyheminthes that is basal to all other lophotrochozoan taxa. Use of 16-state models also appears to reduce the Bayesian support given to certain biologically improbable groups found using standard 4-state models.

Animals↗
PubMed2004

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.

Algorithms↗
PubMedDate not supplied

[Cyclosporin pharmacokinetic modeling in renal transplant patients].

AIM: To characterize the pharmacokinetic behavior of oral cyclosporin (CsA) in renal transplant patient, based on through blood concentration (C0) value, and to develop and to evaluate a Bayesian method for the individualized adjustment of CsA daily dose (DD). METHODS: Sixty-seven renal allograft recipients (42 men and 25 women) who had been treated with CsA (Sandimmun Neoral) associated with mycophenolate mofetil (2g daily) and prednisone (0,5-1 mg/kg daily) were randomly divided into two groups. Group A (N=48) was used to characterize CsA pharmacokinetic behavior and Group B (N=19) to evaluate Bayesian predictive performance for the model developed. We evaluated different structural models using non linear mixed effects modeling implemented in the NONMEN computer program in order to quantify the relationship between DD and C0. Accuracy and precision were evaluated by the mean standardized prediction error and its standard deviation. RESULTS: The Michaelis-Menten model was found to be optimum for quantifying the relationship between DD and C0. This model includes time-dependent parameters such as the Michaelis-Menten constant (Km) and daily maximum dose (Dmax) as well as first order autoregressive terms DD and C0 included in the structural model in an additive way. In the final model, the Dmax parameter is affected by plasmatic urea values and shows a half-life stabilization time of 90.90 days (95% CI: 52.60 to 250 days). Plasmatic urea values of 50 mg/dL are related to an initial Dmax value of 3 mg/kg daily (95% CI: 1.81 to 4.19 mg/kg daily) which decreases exponentially throughout the post-transplant period until it reaches a constant value of 2.16 mg/kg daily (95% CI: 1.41 to 2.91 mg/kg daily) In the same way, the Km parameter presents a central tendency value of 93.60 ng/mL (95% CI: 28.60 to 158.60 ng/mL) and the half-life necessary for its stabilization is 12.70 days (95% CI: 9.80 to 17.90 days). The residual variability of the model is 8.2%. The mean value of standardized prediction errors for populations and its standard deviation, as well as its confidence intervals of 95%, confirm the appropriate accuracy and precision of both a priori and a posteriori predictions with this model. Also, it reached between 70 and 100% a posteriori sequential predictions with prediction errors below 10%. CONCLUSION: The characterization of the pharmacokinetic behavior of CsA requires us to consider parameters such as Dmax and Km as non lineal functions of time, while the first order autoregressive terms DD and C0 must also be incorporated into the model.

Adult↗