Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Bayesian modelling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40Linked to original sources
PubMed2006

Do antidepressants cause suicidality in children? A Bayesian meta-analysis.

BACKGROUND: To quantify the risk of suicidal behavior/ideation (suicidality) for children who use antidepressants, the FDA collected randomized placebo-controlled trials of antidepressant efficacy in children. Although none of the 4487 children completed suicide, 1.7% exhibited suicidality. The FDA meta-analyzed these studies and found sufficient evidence of an increased risk to require a black-box warning on antidepressants for children. PURPOSE: The FDA considered different drug formulations and psychiatric diagnoses to be equivalent in their effect on suicidality. If this assumption does not hold, the FDA analysis may have underestimated the variance of the risk estimate. We investigate the consequences of relaxing these assumptions. METHODS: We extend the FDA analysis using a Bayesian hierarchical model that allows for a study-level component of variability and facilitates extensive sensitivity analyses. RESULTS: We found an association between antidepressant use and an increased risk of suicidality in studies where the diagnosis was major depressive disorder (odds ratio 2.3 [1.3, 3.8]), and where the antidepressant was an SSRI (odds ratio 2.2 [1.3, 3.6]). We did not find evidence for such an association in the complement sets of trials. Although the results based on the hierarchical model are insensitive to model perturbations, the robustness of the FDA's meta-analysis to model assumptions is less clear. These data have limited generalizability due to exclusion of patients with baseline risk of suicide and the use of relatively short duration trials. CONCLUSIONS: Because of model specification and interpretation issues raised in this paper, we conclude that the evidence supporting a causal link between antidepressant use and suicidality in children is weak. The use of Bayesian hierarchical models for meta-analysis has facilitated the incorporation of potentially important sources of variability and the use of sensitivity analysis to assess the consequences of model specifications and their impact on important regulatory decisions.

Antidepressive Agents↗
PubMed1999

Estimation of dynamic parameters from NMR relaxation data using the Lipari-Szabo model-free approach and Bayesian statistical methods.

In order to analyze NMR relaxation data in terms of parameters which describe internal motion, one must first obtain a description of the overall tumbling of the macromolecule in solution. Methods currently used to estimate these global parameters may not always provide reliable estimates of their values and uncertainties. In this paper, we present a general data analysis formalism based on products of Bayesian marginal probability densities which can be used to efficiently combine the information content from multiple experiments, such as R(1), R(2), and NOE data collected at multiple magnetic field strengths, or data from cross-correlation or rotating frame relaxation dispersion experiments. Our approach allows the estimation of global tumbling and internal dynamical parameters and their uncertainties without some of the assumptions which are made in the commonly-used methods for model-selection and global parameter estimation. Compared to an equivalent classical statistical approach, the Bayesian method not only is more computationally efficient, but also provides greater insight into the information content of the data. We demonstrate that this approach can be used to estimate both the isotropic rotational correlation time in the context of the original and "extended" Lipari-Szabo formalisms [Lipari & Szabo, J. Am. Chem. Soc. 1982, 104, 4546; Clore et al., J. Am. Chem. Soc. 1990, 112, 4989], as well as the rotational diffusion coefficients for axially symmetric anisotropic tumbling.

Anisotropy↗
PubMed2005

Bayesian inference for stochastic kinetic models using a diffusion approximation.

This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behavior and the model is identified using equispaced time course data. The estimation framework involves the introduction of m- 1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network.

Bayes Theorem↗
PubMed2006

Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles.

We propose a computational strategy for discovering gene networks affected by a chemical compound. Two kinds of DNA microarray data are assumed to be used: One dataset is short time-course data that measure responses of genes following an experimental treatment. The other dataset is obtained by several hundred single gene knock-downs. These two datasets provide three kinds of information; (i) A gene network is estimated from time-course data by the dynamic Bayesian network model, (ii) Relationships between the knocked-down genes and their regulatees are estimated directly from knock-down microarrays and (iii) A gene network can be estimated by gene knock-down data alone using the Bayesian network model. We propose a method that combines these three kinds of information to provide an accurate gene network that most strongly relates to the mode-of-action of the chemical compound in cells. This information plays an essential role in pharmacogenomics. We illustrate this method with an actual example where human endothelial cell gene networks were generated from a novel time course of gene expression following treatment with the drug fenofibrate, and from 270 novel gene knock-downs. Finally, we succeeded in inferring the gene network related to PPAR-alpha, which is a known target of fenofibrate.

Bayes Theorem↗
PubMed2006

An integrated approach for risk profiling and spatial prediction of Schistosoma mansoni-hookworm coinfection.

Multiple-species parasitic infections are pervasive in the developing world, yet resources for their control are scarce. We present an integrated approach for risk profiling and spatial prediction of coinfection with Schistosoma mansoni and hookworm for western Côte d'Ivoire. Our approach combines demographic, environmental, and socioeconomic data; incorporates them into a geographic information system; and employs spatial statistics. Demographic and socioeconomic data were obtained from education registries and from a questionnaire administered to schoolchildren. Environmental data were derived from remotely sensed satellite images and digitized ground maps. Parasitologic data, obtained from fecal examination by using two different diagnostic approaches, served as the outcome measure. Bayesian variogram models were used to assess risk factors and spatial variation of S. mansoni-hookworm coinfection in relation to demographic, environmental, and socioeconomic variables. Coinfections were found in 680 of 3,578 schoolchildren (19.0%) with complete data records. The prevalence of monoinfections with either hookworm or S. mansoni was 24.3% and 24.1%, respectively. Multinomial Bayesian spatial models showed that age, sex, socioeconomic status, and elevation were good predictors for the spatial distribution of S. mansoni-hookworm coinfection. We conclude that our integrated approach, employing a diversity of data sources, geographic information system and remote sensing technologies, and Bayesian spatial statistics, is a powerful tool for risk profiling and spatial prediction of S. mansoni-hookworm coinfection. More generally, this approach facilitates risk mapping and prediction of other parasite combinations and multiparasitism, and hence can guide integrated disease control programs in resource-constrained settings.

Adolescent↗
PubMed2004

A Bayesian network classification methodology for gene expression data.

We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in the absence of prior expert knowledge. Our classifiers are developed under a cross validation regimen and then validated on corresponding out-of-sample test sets. The classifiers attain a classification rate in excess of 90% on out-of-sample test sets for two publicly available datasets. We present an extensive compilation of results reported in the literature for other classification methods run against these same two datasets. Our results are comparable to, or better than, any we have found reported for these two sets, when a train-test protocol as stringent as ours is followed.

Bayes Theorem↗
PubMed2006

Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.

This paper proposes a new method for estimating kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on adaptive Gaussian Markov random fields. Kinetic parameter estimates using neighboring voxels reduce the observed variability in local tumor regions while preserving sharp transitions between heterogeneous tissue boundaries. Asymptotic results for standard errors from likelihood-based nonlinear regression are compared with those derived from the posterior distribution using Bayesian estimation with and without neighborhood information. Application of the method to the analysis of breast tumors based on kinetic parameters has shown that the use of Bayesian analysis combined with adaptive Gaussian Markov random fields provides improved convergence behavior and more consistent morphological and functional statistics.

Algorithms↗
PubMed1999

Bayesian inference on biopolymer models.

MOTIVATION: Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS: The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY: Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/

Bayes Theorem↗
PubMed2005

Perspectives on the molecular epidemiology of aerodigestive tract cancers.

Improving laboratory techniques and the greater availability of genetic data have led to a flurry of publications from molecular epidemiologic studies on aerodigestive tract cancers. Inconsistent results have been observed in studies of sequence variants, due to limitations such as small sample size, possible detection of false positives, moderate prior probabilities that each SNP confers a substantial increase in cancer risk, and publication bias. Meta- and pooled-analyses were shown to be effective in elucidating modest increases in aerodigestive tract cancer risk attributable to sequence variants. Phenotypic assays developed to quantify an individual's DNA repair capacity have been applied to epidemiological studies on aerodigestive tract cancers. Epigenetic events have also been studied in tumor progression and as susceptibility factors for aerodigestive tract cancers, in smaller scale studies. It is imperative that limitations of previous studies are addressed for future research in the molecular epidemiology of aerodigestive tract cancers. Some recommendations for future research are to: (i) incorporate multiple markers of different types (ex. genotype and phenotype data), (ii) enhance statistical power by conducting studies with larger sample size, and developing consortia to coordinate research efforts, (iii) improve marker selection via a hybrid strategy of incorporating data on evolutionary biology and physico-chemical properties of amino acids, with haplotype/tag SNP data, (iv) employ novel statistical methods such as hierarchical modeling with Bayesian adjustments, false positive reporting probability and modeling of complex pathways. Consortia have been initiated for head and neck cancer (International Head and Neck Cancer Epidemiology Consortium (INHANCE)) and lung cancer (International Lung Cancer Consortium (ILCCO)) with the aim to share comparable data, to focus on rare subgroups such as nonsmokers and to coordinate laboratory analyses. Such collaborative efforts and integration across disciplines will be essential in contributing to the elucidation of genetic susceptibility to aerodigestive tract cancers.

Carcinogens↗
PubMedDate not supplied

A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait.

With the increasing use of survival models in animal breeding to address the genetic aspects of mainly longevity of livestock but also disease traits, the need for methods to infer genetic correlations and to do multivariate evaluations of survival traits and other types of traits has become increasingly important. In this study we derived and implemented a bivariate quantitative genetic model for a linear Gaussian and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted. Model parameters were inferred from their marginal posterior distributions. The required fully conditional posterior distributions were derived and issues on implementation are discussed. The two Weibull baseline parameters were updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation of data. In conclusion, the proposed method allows inferring additive genetic and environmental correlations, and doing multivariate genetic evaluation of a linear Gaussian trait and a survival trait.

Animals↗
PubMed2002

BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes.

PURPOSE: To compare genetic test results for deleterious mutations of BRCA1 and BRCA2 with estimated probabilities of carrying such mutations; to assess sensitivity of genetic testing; and to assess the relevance of other susceptibility genes in familial breast and ovarian cancer. PATIENTS AND METHODS: Data analyzed were from six high-risk genetic counseling clinics and concern individuals from families for which at least one member was tested for mutations at BRCA1 and BRCA2. Predictions of genetic predisposition to breast and ovarian cancer for 301 individuals were made using BRCAPRO, a statistical model and software using Mendelian genetics and Bayesian updating. Model predictions were compared with the results of genetic testing. RESULTS: Among the test individuals, 126 were Ashkenazi Jewish, three were male subjects, 243 had breast cancer, 49 had ovarian cancer, 34 were unaffected, and 139 tested positive for BRCA1 mutations and 29 for BRCA2 mutations. BRCAPRO performed well: for the 150 probands with the smallest BRCAPRO carrier probabilities (average, 29.0%), the proportion testing positive was 32.7%; for the 151 probands with the largest carrier probabilities (average, 95.2%), 78.8% tested positive. Genetic testing sensitivity was estimated to be at least 85%, with false-negatives including mutations of susceptibility genes heretofore unknown. CONCLUSION: BRCAPRO is an accurate counseling tool for determining the probability of carrying mutations of BRCA1 and BRCA2. Genetic testing for BRCA1 and BRCA2 is highly sensitive, missing an estimated 15% of mutations. In the populations studied, breast cancer susceptibility genes other than BRCA1 and BRCA2 either do not exist, are rare, or are associated with low disease penetrance.

Adult↗
PubMed2005

Impact of a general practitioner educational intervention on osteoarthritis treatment in an elderly population.

PURPOSE: We examined whether a continuing medical education intervention increased general practitioners' ability to select the proper pharmacological treatment for patients with osteoarthritis. SUBJECTS AND METHODS: Eight towns in Quebec, Canada were randomly allocated to one of four intervention options, workshop and decision tree, workshop, decision tree, or no intervention. All general practitioners practicing in each town were eligible to participate. We evaluated all dispensed prescriptions for either a cyclooxygenase (COX)-2 inhibitor, nonselective nonsteroidal anti-inflammatory drug or acetaminophen written by eligible general practitioners between May 2000 and June 2001 to elderly patients suffering from osteoarthritis. We used a multi-level Bayesian hierarchical model to assess the impact of the interventions on prescription adequacy. RESULTS: We analyzed 5318 dispensed prescriptions written by 249 general practitioners in the five-month preintervention period and 4610 dispensed prescriptions written by the same physicians in the five-month postintervention period. A score of zero or one was given to every prescription, with one indicating prescription adequacy according to guidelines provided during the interventions. Bayesian hierarchical models showed some improvement in scores in the post- versus preintervention periods in all four groups. The probability of an improvement in the towns allocated the workshop and decision tree over the control was 94%, compared with 74% in the workshop group and 55% in the decision tree group. CONCLUSION: An interactive approach offered by peers and complemented by easy to use guidelines may enhance the general practitioner's ability to manage osteoarthritis patients.

Acetaminophen↗
PubMed1998

Bayesian decision procedures based on logistic regression models for dose-finding studies.

Early-phase clinical trials, conducted to determine the appropriate dose of an experimental drug to take forward to later trials, are considered. The objective is to find the dose associated with some low probability of an adverse event. A Bayesian model is presented, and a decision-theoretic procedure for finding the optimal doses for each of a series of cohorts of subjects is derived. The procedure is flexible and can easily be conducted using standard statistical software. The results of simulations investigating the properties of the procedure are presented.

Bayes Theorem↗
PubMed2006

Singularities affect dynamics of learning in neuromanifolds.

The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cases. The standard statistical paradigm of the Cramér-Rao theorem does not hold, and the singularity gives rise to strange behaviors in parameter estimation, hypothesis testing, Bayesian inference, model selection, and in particular, the dynamics of learning from examples. Prevailing theories so far have not paid much attention to the problem caused by singularity, relying only on ordinary statistical theories developed for regular (nonsingular) models. Only recently have researchers remarked on the effects of singularity, and theories are now being developed. This article gives an overview of the phenomena caused by the singularities of statistical manifolds related to multilayer perceptrons and gaussian mixtures. We demonstrate our recent results on these problems. Simple toy models are also used to show explicit solutions. We explain that the maximum likelihood estimator is no longer subject to the gaussian distribution even asymptotically, because the Fisher information matrix degenerates, that the model selection criteria such as AIC, BIC, and MDL fail to hold in these models, that a smooth Bayesian prior becomes singular in such models, and that the trajectories of dynamics of learning are strongly affected by the singularity, causing plateaus or slow manifolds in the parameter space. The natural gradient method is shown to perform well because it takes the singular geometrical structure into account. The generalization error and the training error are studied in some examples.

Journal Article↗
PubMed1996

A comparison of three techniques for rapid model development: an application in patient risk-stratification.

Accurately risk-stratifying patients is a key component of health care outcomes assessment. And, many health care organizations increasingly are relying upon automated means for assistance in making patient risk-stratification decisions. Unfortunately, the process of outcome model development, as it is currently practiced, is both time consuming and difficult. We investigated the relative abilities of three modeling techniques (logistic regression, artificial neural network (ANN), and Bayesian) to rapidly develop models for risk-stratifying patients. Our results demonstrated that all three modeling techniques perform equally well in certain situations. However, the Bayesian model with conditional independence had the best overall performance. Unfortunately, none of the models were able to achieve the degree of accuracy which would be required in a medical setting.

APACHE↗
PubMed2000

Hierarchical models in generalized synthesis of evidence: an example based on studies of breast cancer screening.

Evidence regarding the potential benefits of a particular health care intervention is often available from a variety of disparate sources. However, formal synthesis of such evidence has traditionally concentrated almost exclusively on that derived from randomized studies, although for a range of conditions the randomized evidence will be less than adequate due to economic, organizational or ethical considerations. In such situations a formal synthesis of the evidence that is available from observational studies can be valuable whilst awaiting higher quality evidence from randomized trials. Consideration of randomized studies alone may be appropriate when assessing the efficacy of an intervention, but assessment of the effectiveness of such an intervention within a more general target population may be improved by consideration of evidence from non-randomized studies as well. Standard meta-analysis methods may allow for both within- and between-study heterogeneity; however when multiple sources of evidence are considered an extra level of complexity is introduced, namely study type. One possible solution to the problem of making inferences, particularly regarding an overall population effect, in such situations is to model the heterogeneity, both quantitative and qualitative, using a Bayesian hierarchical model. The hierarchical nature of such models specifically allows for the quantitative within and between sources of heterogeneity, whilst the Bayesian approach can accommodate a priori beliefs regarding qualitative differences between the various sources of evidence. The use of such methods in practice is illustrated in the context of screening for breast cancer; in this example evidence is available from both randomized clinical trials and observational studies. A particular appeal of a Bayesian approach for this type of problem lies in the prediction of future benefits likely to be observed in a target population. This approach to health service monitoring in general is discussed.

Aged↗
PubMed2002

Bayesian analysis of population PK/PD models: general concepts and software.

Markov chain Monte Carlo (MCMC) techniques have revolutionized the field of Bayesian statistics by enabling posterior inference for arbitrarily complex models. The now widely used WinBUGS software has, over the years, made the methodology accessible to a great many applied scientists, in all fields of research. Despite this, serious application of MCMC methods within the field of population PK/PD has been comparatively limited. We appreciate that for many applied pharmacokineticists the prospect of conducting a Bayesian analysis will require numerous alien concepts to be taken on board and it may be difficult to justify investing the time and effort required in order to understand them (especially since the approach is so computer-intensive). For this reason we provide here a thorough (but often informal) discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD. We also acknowledge that while the WinBUGS software is general purpose, model specification for some types of problem, population PK/PD being a prime example, can be very difficult, to the extent that a specialized interface for describing the problem at hand is often a practical necessity. In the latter part of this paper we describe such an interface, namely PKBugs. A principal aim of the paper is to offer sufficient technical background, in an easy to follow format, that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.

Bayes Theorem↗
PubMed2004

A Bayesian approach for the analysis of panel-count data with dependent termination.

We consider modeling and Bayesian analysis for panel-count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel-count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.

Algorithms↗