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PubMedDate not supplied

Triple-goal estimates for disease mapping.

Maps of regional morbidity and mortality rates play an important role in assessing environmental equity. They provide effective tools for identifying areas with potentially elevated risk, determining spatial trend, and formulating and validating aetiological hypotheses about disease. Bayes and empirical Bayes methods produce stable small-area rate estimates that retain geographic and demographic resolution. The beauty of the Bayesian approach lies in its ability to structure complicated models, inferential goals and analyses. Three inferential goals are relevant to disease mapping and risk assessment: (i) computing accurate estimates of disease rates in small geographic areas; (ii) estimating the distribution of disease rates over the region; (iii) ranking the disease rates so that environmental investigation can be prioritized. No single set of estimates can simultaneously optimize these three goals, and Shen and Louis propose a set of estimates that perform well on all three goals. These are optimal for estimating the distribution of rates and for ranking, and maintain a high accuracy in estimating area-specific rates. However, the Shen/Louis method is sensitive to choice of priors. To address this issue we introduce a robustified version of the method based on a smoothed non-parametric estimate of the prior. We evaluate the performance of this method through a simulation study, and illustrate it using a data set of county-specific lung cancer rates in Ohio.

Algorithms↗
PubMed1996

Population toxicokinetics of tetrachloroethylene.

In assessing the distribution and metabolism of toxic compounds in the body, measurements are not always feasible for ethical or technical reasons. Computer modeling offers a reasonable alternative, but the variability and complexity of biological systems pose unique challenges in model building and adjustment. Recent tools from population pharmacokinetics, Bayesian statistical inference, and physiological modeling can be brought together to solve these problems. As an example, we modeled the distribution and metabolism of tetrachloroethylene (PERC) in humans. We derive statistical distributions for the parameters of a physiological model of PERC, on the basis of data from Monster et al. (1979). The model adequately fits both prior physiological information and experimental data. An estimate of the relationship between PERC exposure and fraction metabolized is obtained. Our median population estimate for the fraction of inhaled tetrachloroethylene that is metabolized, at exposure levels exceeding current occupational standards, is 1.5% [95% confidence interval (0.52%, 4.1%)]. At levels approaching ambient inhalation exposure (0.001 ppm), the median estimate of the fraction metabolized is much higher, at 36% [95% confidence interval (15%, 58%)]. This disproportionality should be taken into account when deriving safe exposure limits for tetrachloroethylene and deserves to be verified by further experiments.

Administration, Inhalation↗
PubMed2002

The pulmonologist's perspective regarding the solitary pulmonary nodule.

The pulmonologist's goal in managing a patient with a solitary pulmonary nodule is to distinguish the benign from malignant nodule and, where malignancy is either confirmed or strongly suspected, to expedite resection. By using established clinical features (eg, age, smoking status) and radiographic findings (eg, calcification, growth rate, size), a probability of malignancy can be determined. If necessary, noninvasive or adjuvant invasive testing is used to alter the probability to one that permits observation or demands resection. The proper use of these tests mandates knowledge about their performance characteristics. Decision-analytic approaches, using Bayesian analysis, may assist with the calculation of probability. These models have not consistently outperformed the clinician or adjuvant testing. The use of low-dose computed tomography (CT) scanning as a screening tool has led to the discovery of many small, indeterminate nodules. Management decisions for these nodules are influenced by their low prevalence of malignancy and small size. Future advances will add to our ability to effectively meet our stated goal.

Decision Making↗
PubMed2004

A Bayesian approach to DNA sequence segmentation.

Many deoxyribonucleic acid (DNA) sequences display compositional heterogeneity in the form of segments of similar structure. This article describes a Bayesian method that identifies such segments by using a Markov chain governed by a hidden Markov model. Markov chain Monte Carlo (MCMC) techniques are employed to compute all posterior quantities of interest and, in particular, allow inferences to be made regarding the number of segment types and the order of Markov dependence in the DNA sequence. The method is applied to the segmentation of the bacteriophage lambda genome, a common benchmark sequence used for the comparison of statistical segmentation algorithms.

Algorithms↗
PubMed2005

Causal protein-signaling networks derived from multiparameter single-cell data.

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.

Algorithms↗
PubMed2005

Performance comparison of two-point linkage methods using microsatellite markers flanking known disease locations.

The Genetic Analysis Workshop 14 simulated data presents an interesting, challenging, and plausible example of a complex disease interaction in a dataset. This paper summarizes the ease of detection for each of the simulated Kofendrerd Personality Disorder (KPD) genes across all of the replicates for five standard linkage statistics. Using the KPD affection status, we have analyzed the microsatellite markers flanking each of the disease genes, plus an additional 2 markers that were not linked to any of the disease loci. All markers were analyzed using the following two-point linkage methods: 1) a MMLS, which is a standard admixture LOD score maximized over theta, alpha, and mode of inheritance, 2) a MLS calculated by GENEHUNTER, 3) the Kong and Cox LOD score as computed by MERLIN, 4) a MOD score (standard heterogeneity LOD maximized over theta, alpha, and a grid of genetic model parameters), and 5) the PPL, a Bayesian statistic that directly measures the strength of evidence for linkage to a marker. All of the major loci (D1-D4) were detectable with varying probabilities in the different populations. However, the modifier genes (D5 and D6) were difficult to detect, with similar distributions under the null and alternative across populations and statistics. The pooling of the four datasets in each replicate (n = 350 pedigrees) greatly improved the chance of detecting the major genes using all five methods, but failed to increase the chance to detect D5 and D6.

Chromosome Mapping↗
PubMed2004

Evaluation of second trimester maternal serum screening for Down's Syndrome using the Spiegelhalter-Knill-Jones (S-KJ) approach.

AIMS: Risk estimation for Down's syndrome in antenatal serum screening with maternal age and multiple serum biomarkers is usually complicated and computationally intensive. We have developed a simple scoring system using the Spiegelhalter-Knill-Jones approach, which was based on Bayesian theorem and the logistic regression model. METHODS: A prospective data set with 3842 singleton pregnancies including 6 affected pregnancies served as "trained data". Maternal age, maternal serum alpha-fetoprotein and human chorionic gonadotrophin levels of each pregnant woman were adopted as the predictors to establish the scoring model using the S-KJ approach. Model validation was undertaken using a receiver operating characteristics (ROC) curve with another 3050 singleton pregnancies including 4 affected pregnancies ("validated data"). RESULTS: For the trained data the sensitivity and specificity of the scoring system at cut-off value of 1:250 was 66.7% and 92.6%, respectively. For the validated data the sensitivity and specificity at the same cut-off point was 75% and 92.2%, respectively. The area under the ROC curve of the trained and validated data was 76.96% (95% CI: 51.80-100%), and 94.07% (95% CI: 84.47-100%), respectively. CONCLUSIONS: The S-KJ scoring system has been demonstrated to be a simple, and efficient method for the risk estimation of Down's syndrome. This system can be applied to other antenatal serum screening systems.

Adult↗
PubMed1998

A semiparametric Bayesian approach to the random effects model.

In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.

Acquired Immunodeficiency Syndrome↗
PubMed2001

Nonparametric mixed effects models for unequally sampled noisy curves.

We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low-rank, low-frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques--Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross-validation--to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.

Algorithms↗
PubMed1998

Modelling behavioral syndromes using Bayesian networks.

In this paper Bayesian networks modelling is applied to a multidimensional model of depression. The characterization of the probabilistic model exploits expert knowledge to associate latent concentrations of neurotransmitters and symptoms. An evolution perspective is also considered. Specific criteria are introduced to detect the influence of the latent variable on the observation of symptoms. The Bayesian analysis is carried out using Gibbs sampling technique which is implemented in the BUGS software. The estimation phase leads to the selection of symptoms entering into the definition of behavioral syndromes. Results on real data are discussed. The last section deals with simulation experiments. Simulation results confirm our methodological choices. Results of the paper can enlarge to the central problem of the management of latent variables in Bayesian networks modelling.

Artificial Intelligence↗
PubMed2003

Variational mixture of Bayesian independent component analyzers.

There has been growing interest in subspace data modeling over the past few years. Methods such as principal component analysis, factor analysis, and independent component analysis have gained in popularity and have found many applications in image modeling, signal processing, and data compression, to name just a few. As applications and computing power grow, more and more sophisticated analyses and meaningful representations are sought. Mixture modeling methods have been proposed for principal and factor analyzers that exploit local gaussian features in the subspace manifolds. Meaningful representations may be lost, however, if these local features are nongaussian or discontinuous. In this article, we propose extending the gaussian analyzers mixture model to an independent component analyzers mixture model. We employ recent developments in variational Bayesian inference and structure determination to construct a novel approach for modeling nongaussian, discontinuous manifolds. We automatically determine the local dimensionality of each manifold and use variational inference to calculate the optimum number of ICA components needed in our mixture model. We demonstrate our framework on complex synthetic data and illustrate its application to real data by decomposing functional magnetic resonance images into meaningful-and medically useful-features.

Bayes Theorem↗
PubMed2005

Prediction of protein interdomain linker regions by a hidden Markov model.

MOTIVATION: Our aim was to predict protein interdomain linker regions using sequence alone, without requiring known homology. Identifying linker regions will delineate domain boundaries, and can be used to computationally dissect proteins into domains prior to clustering them into families. We developed a hidden Markov model of linker/non-linker sequence regions using a linker index derived from amino acid propensity. We employed an efficient Bayesian estimation of the model using Markov Chain Monte Carlo, Gibbs sampling in particular, to simulate parameters from the posteriors. Our model recognizes sequence data to be continuous rather than categorical, and generates a probabilistic output. RESULTS: We applied our method to a dataset of protein sequences in which domains and interdomain linkers had been delineated using the Pfam-A database. The prediction results are superior to a simpler method that also uses linker index.

Algorithms↗
PubMed1999

Stochastic algorithms for Markov models estimation with intermittent missing data.

Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for estimating Markov chain models in the case of intermittent missing data in longitudinal studies, a stochastic EM algorithm and the Gibbs sampler, are described. The first can be viewed as a random perturbation of the EM algorithm and is appropriate when the M step is straightforward but the E step is computationally burdensome. It leads to a good approximation of the maximum likelihood estimates. The Gibbs sampler is used for a full Bayesian inference. The performances of the two algorithms are illustrated on two simulated data sets. A motivating example concerned with the modelling of the evolution of parasitemia by Plasmodium falciparum (malaria) in a cohort of 105 young children in Cameroon is described and briefly analyzed.

Algorithms↗
PubMedDate not supplied

Medical expert systems based on causal probabilistic networks.

Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other methods. A CPN is an intensional model of a domain, and it is therefore conceptually much closer to qualitative reasoning systems and to simulation systems than to rule-based or logic-based systems. Recent progress in Bayesian inference in networks has yielded computationally efficient methods. The inference method used follows the fundamental axioms of probability theory, and gives a sound framework for causal and diagnostic (deductive and abductive) reasoning under uncertainty. Experience with the prototypes indicates that it may be possible to use decision theory as a rational approach to test planning and therapy planning. The way in which knowledge is acquired and represented in CPNs makes it easy to express 'deep knowledge' for example in the form of physiological models, and the facilities for learning make it possible to make a smooth transition from expert opinion to statistics based on empirical data.

Artificial Intelligence↗
PubMed1997

DIAVAL, a Bayesian expert system for echocardiography.

DIAVAL is an expert system for the diagnosis of heart diseases, including several kinds of data, mainly from echocardiography. The first part of this paper is devoted to the causal probabilistic model which constitutes the knowledge base of the expert system in the form of a Bayesian network, emphasizing the importance of the OR gate. The second part deals with the process of diagnosis, which consists of computing the a posteriori probabilities, selecting the most probable and most relevant diagnoses, and generating a written report. It also describes the results of the evaluation of the program.

Artificial Intelligence↗
PubMed1996

Temporal reasoning for diagnosis in a causal probabilistic knowledge base.

We have added temporal reasoning to the Heart Disease Program (HDP) to take advantage of the temporal constraints inherent in cardiovascular reasoning. Some processes take place over minutes while others take place over months or years and a strictly probabilistic formalism can generate hypotheses that are impossible given the temporal relationships involved. The HDP has temporal constraints on the causal relations specified in the knowledge base and temporal properties on the patient input provided by the user. These are used in two ways. First, they are used to constrain the generation of the pre-computed causal pathways through the model that speed the generation of hypotheses. Second, they are used to generate time intervals for the instantiated nodes in the hypotheses, which are matched and adjusted as nodes are added to each evolving hypothesis. This domain offers a number of challenges for temporal reasoning. Since the nature of diagnostic reasoning is inferring a causal explanation from the evidence, many of the temporal intervals have few constraints and the reasoning has to make maximum use of those that exist. Thus, the HDP uses a temporal interval representation that includes the earliest and latest beginning and ending specified by the constraints. Some of the disease states can be corrected but some of the manifestations may remain. For example, a valve disease such as aortic stenosis produces hypertrophy that remains long after the valve has been replaced. This requires multiple time intervals to account for the existing findings. This paper discusses the issues and solutions that have been developed for temporal reasoning integrated with a pseudo-Bayesian probabilistic network in this challenging domain for diagnosis.

Artificial Intelligence↗
PubMed2005

A Bayesian approach to the estimation of ancestral genome arrangements.

We describe a Bayesian approach to estimate phylogeny and ancestral genome arrangements on the basis of genome arrangement data using a model in which gene inversion is the sole mechanism of change. While we have described a similar method to estimate phylogenetic relationships in the statistics literature, the novel contribution of the present work is the description of a method to compute probability distributions of ancestral genome arrangements. We assess the robustness of posterior distributions to different specifications of prior distributions and provide an empirical means to selecting a prior distribution. We note that parsimony approaches to ancestral reconstruction in the literature focus on the development of computationally efficient algorithms for searching for optimal ancestral genome arrangements, but, unlike Bayesian approaches, do not include assessment of uncertainty in these estimates. We compare and contrast a Bayesian approach with a parsimony approach to infer phylogenies and ancestral arrangements from genome arrangement data by re-analyzing a number of previously published data sets.

Algorithms↗
PubMed1999

Population pharmacokinetics of gentamicin in preterm neonates: evaluation of a once-daily dosage regimen.

Population pharmacokinetic parameters of gentamicin in preterm neonates on a once-daily dosage regimen of 3.0 mg/kg given intravenously every 24 hours were established prospectively. In 34 preterm neonates with a mean gestational age of 32 +/- 4 (SD), 182 serum gentamicin levels (91 peak/trough pairs) were determined. Individual adjustments of dose or dosage interval were calculated by computer-aided Bayesian forecasting. The parameters Vd, ke, and CL for each patient were obtained by the nonparametric estimation of maximization method. The predictive power of the model was calculated and the pharmacokinetic estimates were statistically analyzed with SPSS/PC. Cluster analysis showed a division into 2 subpopulations (designated 1 and 2) on the basis of postnatal age. The mean +/- SD postnatal age of subpopulation 1 (n = 29) was 6 +/- 2 days (range 1-7) and of subpopulation 2 (n = 5) 15 +/- 4 days (range 12-24). The mean +/- SD gentamicin relative clearances of subpopulation 1 and subpopulation 2 were 0.0515 +/- 0.0128 and 0.1026 +/- 0.0102 L kg(-1) hr(-1), respectively (p < 0.05). The mean +/- SD values for Vd (Lkg(-1)) in both populations 1 and 2 were 0.6916 +/- 0.1670 and 0.7509 +/- 0.1961, respectively (not significantly different). For ke these data were 0.0744 +/- 0.0200 and 0.1366 +/- 0.0522 (p < 0.05). Statistics showed that the data for Vd and ke of subpopulation 1 were normally distributed (Vd and ke skewness 1.61 and 1.46; kurtosis 3.09 and 3.10 respectively). The model yielded a bias of -0.11 mg/L and a precision of 0.36 mg/L. It is recommended that gentamicin be started in a dosage of 3.5 mg/kg intravenously once-daily under close monitoring.

Age Factors↗