SEARCH · PubMed Health

Results for “Bayesian analysis”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 883 records · Page 49Linked to original sources

PubMed2001

A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes.

MOTIVATION: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. RESULTS: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t -test, provide a systematic inference approach that compares favorably with simple t -test or fold methods, and partly compensate for the lack of replication.

Bayes Theorem↗

PubMed2003

[Quantitative basis for medical imaging analysis: Information theory and Bayesian inference].

One of the main issues in radiology is image analysts and report. The two stages of this process are the extraction of the information from the image and the diagnostic exercise. This paper is a theoretical analysis of image interpretation from a quantitative perspective, using information theory and Bayesian inference, and relating it to the concepts of evidence based medicine. It is concluded that the amount of information is dependent on the previous state of knowledge and the acquaintance with the clinical history and complementary exams. The three determined stages of the diagnostic process are a priori, conditioned by the image and a posteriori analyses. The first stage is related to the degree of information and knowledge of the case. The second stage is dependent on the conditional probability for the presence of image signs of the disease and it is thus directly dependent on the sensitivity and specificity of the exam. The third stage is dependent of the previous stages, that in the simplest case can be expressed in function of the odds or likelihood ratios. These three stages allow to reach a decision about the usefulness of a given exam and they orient the questions that a given image may answer. They also orient the research on image analysis for an adequate decision making, minimizing diagnostic uncertainty.

Bayes Theorem↗

PubMed2004

Improving Cox survival analysis with a neural-Bayesian approach.

In this article we show that traditional Cox survival analysis can be improved upon when supplemented with sensible priors and analysed within a neural Bayesian framework. We demonstrate that the Bayesian method gives more reliable predictions, in particular for relatively small data sets. The obtained posterior (the probability distribution of network parameters given the data) which in itself is intractable, can be made accessible by several approximations. We review approximations by Hybrid Markov Chain Monte Carlo sampling, a variational method and the Laplace approximation. We argue that although each Bayesian approach circumvents the shortcomings of the original Cox analysis, and therefore yields better predictive results, in practice the use of variational methods or Laplace is preferable. Since Cox survival analysis is infamous for its poor results with (too) many inputs, we use the Bayesian posterior to estimate p-values on the inputs and to formulate an algorithm for backward elimination. We show that after removal of irrelevant inputs Bayesian methods still achieve significantly better results than classical Cox.

Antineoplastic Agents↗

PubMed2006

Bayesian semi-parametric ROC analysis.

This paper describes a semi-parametric Bayesian approach for estimating receiver operating characteristic (ROC) curves based on mixtures of Dirichlet process priors (MDP). We address difficulties in modelling the underlying distribution of screening scores due to non-normality that may lead to incorrect choices of diagnostic cut-offs and unreliable estimates of prevalence of the disease. MDP is a robust tool for modelling non-standard diagnostic distributions associated with imperfect classification of an underlying diseased population, for example, when a diagnostic test is not a gold standard. For posterior computations, we propose an efficient Gibbs sampling framework based on a finite-dimensional approximation to MDP. We show, using both simulated and real data sets, that MDP modelling for ROC curve estimation closely parallels the frequentist kernel density estimation (KDE) approach.

Adolescent↗

PubMed2006

Comprehensive decision-analytic model and Bayesian value-of-information analysis: pentoxifylline in the treatment of chronic venous leg ulcers.

OBJECTIVE: To conduct a Bayesian value-of-information analysis of the cost effectiveness of pentoxifylline (vs placebo) as an adjunct to compression for venous leg ulcers. METHODS: A probabilistic Markov model was developed to estimate mean clinical benefits and costs associated with oral pentoxifylline (400mg three times daily) and placebo. Clinical data were obtained from a systematic review and synthesised using Bayesian methods. The decision uncertainty associated with the adoption of pentoxifylline as well as the maximum value associated with further research were estimated before and after the completion of the largest 'definitive' treatment trial. Resource use was obtained from a UK national audit and unit costs applied (pounds, 2004 values). RESULTS: The prior and posterior analyses suggest that pentoxifylline is a dominant therapy versus placebo. In the prior analysis, patients in the pentoxifylline group healed an average of 8.28 weeks quicker than patients in the placebo group (95% credibility interval [CI] 1.89, 14.56), had a 0.02 gain in QALYs (95% CI -0.12, 0.17) and an average reduction in cost of 153.4 pounds (95% CI -53.11, 354.9). Estimates of the uncertainty surrounding the cost effectiveness of pentoxifylline and the value of perfect information in both analyses did not suggest further research was justified. In the prior analysis, for willingness-to-pay values of 0 pounds, 100 pounds and 500 pounds per QALY gained, the estimated values of perfect information were 128,200 pounds, 127,100 pounds and 126,700 pounds, respectively. Incorporation of the information from the largest randomised controlled trial on pentoxifylline did improve the estimate of the clinical effect associated with this drug; however, the variation was not large enough to reverse either the decision regarding the dominance of pentoxifylline or the maximum value associated with further research. CONCLUSION: Bayesian value-of-information analysis represents a valuable tool for healthcare decision making. Had the results from this analysis been available before the largest trial was funded, a more efficient allocation of research and development resources could have been made.

Administration, Oral↗

PubMed2004

Investigation into postoperative endophthalmitis and lessons learned.

PURPOSE: To describe an approach to the investigation of a series of endophthalmitis cases to determine whether there was a true outbreak. SETTING: Outpatient facility, Sunderland, England. METHODS: Different approaches to statistical analysis of the probability of infrequent events being due to chance occurrence are described. Potential factors leading to an outbreak were reviewed. RESULTS: Bayesian statistical analysis was shown to be appropriate in the determination of an endophthalmitis outbreak. The only factor found to account for the outbreak was the operating surgeon's recent abandonment of subconjunctival antibiotic prophylaxis. This decision was based on the absence of good evidence that subconjunctival antibiotic injection is effective in prophylaxis. These endophthalmitis cases demonstrate that the absence of evidence for effect is not the same as there being no effect. CONCLUSIONS: Bayesian statistical analysis has a place in determining whether an outbreak has occurred. Withdrawing treatment simply on the basis of a lack of good evidence can lead to undesirable outcomes.

Aged↗

PubMed2004

Prior convictions: Bayesian approaches to the analysis and interpretation of clinical megatrials.

Large, randomized clinical trials ("megatrials") are key drivers of modern cardiovascular practice, since they are cited frequently as the authoritative foundation for evidence-based management policies. Nevertheless, fundamental limitations in the conventional approach to statistical hypothesis testing undermine the scientific basis of the conclusions drawn from these trials. This review describes the conventional approach to statistical inference, highlights its limitations, and proposes an alternative approach based on Bayes' theorem. Despite its inherent subjectivity, the Bayesian approach possesses a number of practical advantages over the conventional approach: 1). it allows the explicit integration of previous knowledge with new empirical data; 2). it avoids the inevitable misinterpretations of p values derived from megatrial populations; and 3). it replaces the misleading p value with a summary statistic having a natural, clinically relevant interpretation-the probability that the study hypothesis is true given the observations. This posterior probability thereby quantifies the likelihood of various magnitudes of therapeutic benefit rather than the single null magnitude to which the p value refers, and it lends itself to graphical sensitivity analyses with respect to its underlying assumptions. Accordingly, the Bayesian approach should be employed more widely in the design, analysis, and interpretation of clinical megatrials.

Bayes Theorem↗

PubMed2006

Inter-rater reliability of nursing home surveys: a Bayesian latent class approach.

In the U.S., federal and state governments perform routine inspections of nursing homes. Results of the inspections allow government to generate fines for findings of non-compliance as well as allow consumers to rank facilities. The purpose of this study is to investigate the inter-rater reliability of the nursing home survey process. In general, the survey data involves 191 binary deficiency variables interpreted as 'deficient' or 'non-deficient'. To reduce the dimensionality of the problem, our proposed method involves two steps. First, we reduce the deficiency categories to sub-categories using previous nursing home studies. Second, looking at the State of Kansas specifically, we take the deficiency data from 1 year, and use Bayesian latent class analysis (LCA) to collapse the sub-categories to a binary variable. We evaluate inter-rater agreement using deficiency data from two separate survey teams on one facility, a matched-pair design. We evaluate the agreement of the two raters on binary data using the weights from the LCA. This allows a two-by-two contingency analysis using a Bayesian beta-binomial model. We elicit informative prior distributions from the nursing home providers. Together, with the experimental data, this provides a posterior distribution of the kappa agreement of the raters for nursing home deficiency citation data.

Bayes Theorem↗

PubMed2000

Goodness-of-Fit Analysis of Radial Velocity Surveys.

Using eigenmode expansion of the Mark III and SFI surveys of cosmological radial velocities, a goodness-of-fit analysis is applied on a mode-by-mode basis. This differential analysis complements the Bayesian maximum likelihood analysis that finds the most probable model given the data. Analyzing the surveys with their corresponding most likely models from the CMB-like family of models, as well as with the currently popular LambdaCDM model, reveals a systematic inconsistency of the data with these "best" models. There is a systematic trend of the cumulative chi(2) to increase with the mode number (where the modes are sorted by decreasing order of the eigenvalues). This corresponds to a decrease of the chi(2) with the variance associated with a mode and hence with its effective scale. It follows that the differential analysis finds that on small (large) scales the global analysis of all the modes "puts" less (more) power than actually required by the data. This observed trend might indicate one of the following: (1) the theoretical model (i.e., power spectrum) or the error model (or both) have an excess of power on large scales, (2) velocity bias, or (3) the velocity data suffers from systematic errors that have not yet been corrected.

Journal Article↗

PubMed2000

Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis.

Cancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors. Specifically, we propose 4 tree models for tumor progression inferred from the large ovarian cancer data set described in the first 2 articles in this series. The models are derived from 2 different methods to select the non-random genetic aberrations and 2 different methods to infer the trees, given a set of events. Various aspects of the tree models are tested and extended by 5 methods: overall tests of independence, likelihood ratio tests, principal components analysis, directed acyclic graph modeling, and Bayesian survival analysis. All our methods lead to strikingly consistent conclusions about chromosomal breakpoints in ovarian adenocarcinoma, including (1) the non-random breakpoints in ovarian adenocarcinoma do not occur independently; (2) breakpoints in regions 1p3 and 11p1 are important early events and distinguish a class of tumors associated with poor prognosis; and (3) breakpoints in 1p1, 3p1, and 1q2 distinguish a class of ovarian tumors, and the breaks at 1p1 and 3p1 are associated with poor prognosis.

Adenocarcinoma↗

PubMed2005

The influence of weight with assay error on gentamicin pharmacokinetics using the Bayesian and nonlinear least square regression analysis in appendicitis patients.

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appendicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected 48 h after the first dose from all patients at the following times, just before the regularly scheduled infusion, at 0.5 h and 2 h after the end of the 0.5 h infusion. Serum gentamicin levels were analysed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12 and 16 microg/ml in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (microg/ml) = 0.0246-(0.0495C) + (0.00203C(2)). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and the better, safer care of patients receiving gentamicin.

Anti-Bacterial Agents↗

PubMed2005

The influence of assay error weight on gentamicin pharmacokinetics using the Bayesian and nonlinear least square regression analysis in appendicitis patients.

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appendicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected at 48 h after the first dose from all patients at the following times, just before regularly scheduled infusion, at 0.5 h and 2 h after the end of 0.5 h infusion. Serum gentamicin levels were analyzed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12, and 16 microg/mL in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (microg/mL) = 0.0246 - (0.0495C) + (0.00203C2). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and better, safer care of patients receiving gentamicin.

Anti-Bacterial Agents↗

PubMed2006

Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK).

Recent advances in disease mapping have focused first on including the time dimension, thus giving rise to spatio-temporal analysis of the variation of disease risk and, secondly, on carrying out joint analysis of two diseases that share common environmental risk factors and are, therefore, related. Here, we try to combine both issues and present a joint analysis of the spatio-temporal variation of the risks of two related diseases processes-male and female lung cancer incidence-in a region of England. To do so, we use a Bayesian hierarchical model that splits the risk of disease into two spatio-temporal components: a shared component and a specific component that calibrates the differential between the two diseases.

Bayes Theorem↗

PubMed2026

Identification and genetic validation of potential therapeutic targets for pulmonary hypertension through multi-omics causal inference.

Pulmonary hypertension (PH) underscores the urgent need for novel therapeutic targets. This study aimed to employ a proteome-wide Mendelian randomization (MR) approach to systematically identify circulating proteins causally associated with PH, thereby providing genetically validated candidate targets for drug development. We adopted a 2-sample MR design, integrating large-scale plasma proteomic quantitative trait loci (pQTL) data (encompassing 4148 proteins) and summary statistics from a large-scale PH genome-wide association study (2047 cases, 8301 controls). Candidate targets were screened through a multilayered analytical pipeline comprising proteomic MR, transcriptomic MR, and summary-data-based Mendelian randomization. The ultimately identified MR-Identified Causal Candidate Targets (MR-ICTs) underwent rigorous Bayesian colocalization analysis, followed by biological characterization through functional enrichment analysis, single-cell transcriptomics, and phenome-wide association studies. Through robust genetic causal inference, this study provides that circulating proteins such as LYZ, GREM2, NID1, and PF4V1 play causal roles in PH pathogenesis. These findings offer a set of rigorously genetically validated, high-priority therapeutic targets for developing novel PH treatments, specifically addressing key pathological mechanisms such as innate immunity, BMP signaling pathway dysregulation, and platelet activation. Our multi-dimensional analysis ultimately identified 6 MR-ICTs causally associated with PH. Notably, the causal associations for lysozyme C (LYZ), gremlin-2 (GREM2), nidogen-1 (NID1), and platelet factor 4 variant 1 (PF4V1) were stringently validated by Bayesian colocalization analysis (posterior probability for hypothesis 4 [PPH4], indicating a shared causal variant, > 0.99). Functional enrichment analysis revealed significant involvement of these targets in immune response and TGF-β signaling pathways. Single-cell analysis further elucidated their cell-type-specific expression, with LYZ predominantly expressed in monocytes and PF4V1 almost exclusively in platelets.

Hypertension, Pulmonary↗

PubMed2002

Bayesian nonstationary autoregressive models for biomedical signal analysis.

We describe a variational Bayesian algorithm for the estimation of a multivariate autoregressive model with time-varying coefficients that adapt according to a linear dynamical system. The algorithm allows for time and frequency domain characterization of nonstationary multivariate signals and is especially suited to the analysis of event-related data. Results are presented on synthetic data and real electroencephalogram data recorded in event-related desynchronization and photic synchronization scenarios.

Algorithms↗

PubMed2004

Phylogenetic analysis of the nuclear alcohol dehydrogenase (Adh) gene family in Carex section Acrocystis (Cyperaceae) and combined analyses of Adh and nuclear ribosomal ITS and ETS sequences for inferring species relationships.

We analyzed sequence variation for the alcohol dehydrogenase (Adh) gene family in Carex section Acrocystis (Cyperaceae) to reconstruct Adh gene trees for Acrocystis species and to characterize the structure of the Adh gene family in Carex. Two Adh loci were included with ITS and ETS sequences in a combined Bayesian inference analysis of Carex section Acrocystis to gain a better understanding of species relationships in the section. In addition, we comment on how the results presented here contribute to our knowledge of the birth-death process of the Adh gene family in angiosperms. It appears that the structure of the Adh gene family in Carex is complex with possibly six loci present in the gene family. Additionally, variation among Acrocystis species within loci is quite low, and there is little phylogenetic resolution in the individual datasets. Bayesian inference analysis of the combined ITS, ETS, Adh1, and Adh2 datasets resulted in a moderately well-supported phylogenetic hypothesis of relationships in the section which is discussed in relation to previous hypotheses of relationships.

Alcohol Dehydrogenase↗

PubMed2000

Genetic basis of climatic adaptation in scots pine by bayesian quantitative trait locus analysis.

We examined the genetic basis of large adaptive differences in timing of bud set and frost hardiness between natural populations of Scots pine. As a mapping population, we considered an "open-pollinated backcross" progeny by collecting seeds of a single F(1) tree (cross between trees from southern and northern Finland) growing in southern Finland. Due to the special features of the design (no marker information available on grandparents or the father), we applied a Bayesian quantitative trait locus (QTL) mapping method developed previously for outcrossed offspring. We found four potential QTL for timing of bud set and seven for frost hardiness. Bayesian analyses detected more QTL than ANOVA for frost hardiness, but the opposite was true for bud set. These QTL included alleles with rather large effects, and additionally smaller QTL were supported. The largest QTL for bud set date accounted for about a fourth of the mean difference between populations. Thus, natural selection during adaptation has resulted in selection of at least some alleles of rather large effect.

Acclimatization↗

PubMed2004

Bayesian models for the analysis of genetic structure when populations are correlated.

MOTIVATION: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations. RESULTS: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a dataset of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to overestimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework. CONTACT: fur@ohsu.edu.

Bayes Theorem↗