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Results for “Bayesian analysis”

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At least 1,135 records · Page 63Linked to original sources
PubMed2006

Effect of early and late GB virus C viraemia on survival of HIV-infected individuals: a meta-analysis.

OBJECTIVES: To conduct a meta-analysis to synthesize the evidence regarding the effect of co-infection with GB virus C (GBV-C) on survival of HIV-infected individuals, and to estimate the effect. METHODS: A Bayesian meta-analysis was conducted to synthesize evidence from eligible studies. Prospective survival studies of HIV-1-infected individuals, with outcome defined as time from baseline to all-cause death, were included and classified by whether GBV-C status was determined in early or late HIV disease. The primary measure was the hazard ratio (HR) of death for HIV-infected individuals with GBV-C infection versus those without GBV-C infection. RESULTS: Eleven studies from eight publications met the inclusion criteria. For studies with GBV-C status measured 2 years or less after HIV seroconversion (912 subjects), the combined HR was 0.88 [95% credible interval (CI) 0.30, 1.50]. For studies with GBV-C status measured more than 2 years after HIV seroconversion (1294 subjects), the combined HR was 0.41 (95% CI 0.23, 0.69). CONCLUSIONS: No conclusive evidence was found of an association between survival and GBV-C infection early in HIV disease. However, when GBV-C infection was present later in HIV disease, a significant reduction in the hazard for mortality was observed for those with co-infection. Potential explanations for this difference include a non-proportional benefit of GBV-C over time, possibly related to clearance of GBV-C infection early in HIV disease. The timing of GBV-C infection appears to account for the contradictory results of studies on the effect of GBV-C coinfection on survival of HIV-infected people.

Adult↗
PubMed2005

A Bayesian hierarchical non-linear regression model in receiver operating characteristic analysis of clustered continuous diagnostic data.

Receiver operating characteristic (ROC) analysis is a useful evaluative method of diagnostic accuracy. A Bayesian hierarchical nonlinear regression model for ROC analysis was developed. A validation analysis of diagnostic accuracy was conducted using prospective multi-center clinical trial prostate cancer biopsy data collected from three participating centers. The gold standard was based on radical prostatectomy to determine local and advanced disease. To evaluate the diagnostic performance of PSA level at fixed levels of Gleason score, a normality transformation was applied to the outcome data. A hierarchical regression analysis incorporating the effects of cluster (clinical center) and cancer risk (low, intermediate, and high) was performed, and the area under the ROC curve (AUC) was estimated.

Bayes Theorem↗
PubMed2006

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance.

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.

Amino Acid Sequence↗
PubMedDate not supplied

Representation and analysis of medical decision problems with influence diagrams.

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. They use clinical examples to illustrate the mathematical operations of the influence-diagram-evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphic representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis.

AIDS-Related Opportunistic Infections↗
PubMed2026

Oncotype DX-guided vs physician-directed chemotherapy and survival in HR+/HER2- breast cancer.

BACKGROUND: Oncotype DX testing guides adjuvant chemotherapy decisions in early-stage hormone receptor-positive/HER2-negative breast cancer, but testing is not universally performed, and outcomes associated with genomic-informed versus clinicopathologic-based chemotherapy decision pathways remain unclear. METHODS: Using the 2022 National Cancer Database Breast Participant User File, we identified women diagnosed from 2010 to 2022 with pathologic T1b-T2, node-negative, hormone receptor-positive/HER2-negative invasive breast cancer who received adjuvant chemotherapy and endocrine therapy. Patients were classified into an Oncotype-guided group, defined by Oncotype DX testing with a recurrence score of 26 or higher, and a physician-directed group, defined by receipt of chemotherapy without genomic testing. The primary outcome was overall survival. Analyses used multivariable Cox models, logistic-IPTW and MLP-IPTW, restricted mean survival time analysis, and a Bayesian latent confounding survival model. RESULTS: Among 56,625 women, 27,278 were in the Oncotype-guided group and 29,347 in the physician-directed group. Median ages were 59 and 56 years, respectively. The Oncotype-guided group had more favorable overall survival than the physician-directed group in multivariable Cox analysis (HR, 0.906; 95% CI, 0.856-0.959; P&#x202f;<&#x202f;0.001), with similar findings in IPTW analyses. The association was concentrated among patients aged 56 years or older (HR, 0.866; 95% CI, 0.809-0.927; P&#x202f;<&#x202f;0.001). The Bayesian model showed no strong residual confounding signal. CONCLUSIONS: Among chemotherapy-treated women, an Oncotype-guided pathway was associated with more favorable overall survival than a physician-directed pathway, particularly among older patients, which indicating prognostic heterogeneity selected using genomic versus conventional clinicopathologic information.

Humans↗
PubMed2001

Multilevel models for meta-analysis, and their application to absolute risk differences.

Meta-analysis can be considered a multilevel statistical problem, since information within studies is combined in the presence of potential heterogeneity between studies. Here a general multilevel model framework is developed for meta-analysis to combine either summary data or individual patient outcome data from each study, and to include either study or individual level covariates that might explain heterogeneity. Classical and Bayesian approaches to estimation are contrasted. These methods are applied to a meta-analysis of trials of thrombolytic therapy after myocardial infarction. Subgroups within the trials were available, categorized by the time delay until treatment, so that a three-level random effects model that includes time delay as a covariate is proposed. In addition it was desired to represent the treatment effect as an absolute risk reduction, rather than the conventional odds ratio. We show how this can be achieved within a Bayesian analysis, while still recognizing the binary nature of the original outcome data.

Bayes Theorem↗
PubMed2003

Multivariate meta-analysis.

Meta-analysis is now a standard statistical tool for assessing the overall strength and interesting features of a relationship, on the basis of multiple independent studies. There is, however, recent acknowledgement of the fact that in many applications responses are rarely uniquely determined. Hence there has been some change of focus from a single response to the analysis of multiple outcomes. In this paper we propose and evaluate three Bayesian multivariate meta-analysis models: two multivariate analogues of the traditional univariate random effects models which make different assumptions about the relationships between studies and estimates, and a multivariate random effects model which is a Bayesian adaptation of the mixed model approach. Our preferred method is then illustrated through an analysis of a new data set on parental smoking and two health outcomes (asthma and lower respiratory disease) in children.

Asthma↗
PubMed2005

Bayesian error-in-variable survival model for the analysis of GeneChip arrays.

DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error-in-variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.

Bayes Theorem↗
PubMed1984

The radiology of nontraumatic coma.

This article describes a logical approach to the neuroimaging evaluation of patients with coma. The decision tree analysis provides an efficient method and attempts to convey the logical sequential concepts of analysis of CCT findings in patients presenting in stupor or coma. The sequential approach tabulated serves as a good mnemonic for clinicians faced daily with decisions to be made in the evaluation and management of comatose patients. The approach also provides a framework for subsequent statistical (Bayesian or other) analysis of efficacy, cost effectiveness, and impact on clinical practice and health care delivery.

Atrophy↗
PubMed2006

Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules.

MOTIVATION: Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis. RESULTS: By analyzing five datasets, we demonstrate that among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods [e.g. replacing MVs by zero, K nearest-neighbor (KNN) imputation algorithm, local least square imputation and Bayesian principal component analysis], while the classification and regression tree classifiers are sensitive in terms of classification accuracy. The KNNclassifiers built on differentially expressed genes (DEGs) are robust to the varied MV treatments, but the performances of the KNN classifiers based on all measured genes can be significantly deteriorated when imputing MVs for genes with larger missing rate (MR) (e.g. MR > 5%). Generally, while replacing MVs by zero performs relatively poor, the other imputation algorithms have little difference in affecting classification performances of the SVM or KNN classifiers. We further demonstrate the power and feasibility of our recently proposed functional expression profile (FEP) approach as means to handle microarray data with MVs. The FEPs, which are derived from the functional modules that are enriched with sets of DEGs and thus can be consistently identified under varied MV treatments, achieve precise disease classification with better biological interpretation. We conclude that the choice of MV treatments should be determined in context of the later approaches used for disease classification. The suggested exclusion criterion of ignoring the genes with larger MR (e.g. >5%), while justifiable for some classifiers such as KNN classifiers, might not be considered as a general rule for all classifiers.

Algorithms↗
PubMed2003

Meta-analysis to determine the incidence of obstetric anal sphincter damage.

BACKGROUND: The reported incidence of anal sphincter injury after first (11.5-35.0 per cent) and subsequent (3.4-12.1 per cent) vaginal deliveries varies widely. In addition, the reported incidence of associated faecal incontinence ranges from zero to 68.2 per cent. The aim of this study was to perform a meta-analysis of reported incidences of postpartum anal sphincter defect diagnosed by endoanal ultrasonography (EAUS) and associated incidences of faecal incontinence. METHODS: A Medline search yielded five studies with more than 100 subjects who underwent EAUS after childbirth for evaluation of anal sphincter disruption and who were questioned about symptoms of faecal incontinence, defined as any impairment in flatus and stool control but not including urgency of defaecation. A Bayesian meta-analysis was performed to produce one inference while accounting for potential heterogeneity among the five study populations. RESULTS: Meta-analysis of 717 vaginal deliveries revealed a 26.9 per cent incidence of anal sphincter defect in primiparous women and an 8.5 per cent incidence of new sphincter defects in multiparous women. Overall, 29.7 per cent of anal sphincter defects were symptomatic. Some 3.4 per cent of women experienced postpartum faecal incontinence without an anal sphincter defect. In a Bayesian calculation, the probability of postpartum faecal incontinence due to a sphincter defect was 76.8-82.8 per cent. CONCLUSION: : The incidence of occult anal sphincter disruption following vaginal delivery is much higher than commonly estimated. However, at least two-thirds of occult defects are asymptomatic postpartum. The probability of faecal incontinence associated with an anal sphincter defect was 76.8-82.8 per cent.

Anal Canal↗
PubMed2005

Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.

PURPOSE: We previously reported the use of clustering by unsupervised learning with machine learning classifiers to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma. In this study, the process of unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated. METHODS: SAP fields were obtained with the Humphrey Visual Field Analyzer on 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photos. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, it learned an optimal number of maximally independent axes for each cluster. RESULTS: The most informative number of clusters was two. vB-ICA-mm placed 68.6% of the SAP fields from eyes with GON in a cluster labeled G and 98.4% of the fields from eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at -1 SD and +2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity as they were located farther in the positive direction from the cluster G mean. CONCLUSIONS: vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data mining technique for new and unfamiliar complex tests.

Artificial Intelligence↗
PubMed2005

Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.

Apolipoproteins E↗
PubMed2005

[Phylogenetic analysis for H3A1 strain of all human influenza A virus].

OBJECTIVE: Influenza A virus remains an important pathogen which threatens humans. With the help of latest developed bioinformatics tools, all available human Influenza A virus H3A1 strains were explored to deeply understanding its evolution and variation rules. METHODS: All data of H3A1 sequence in NCBI Genbank and Influenza sequence database were downloaded and aligned in ClustalX with two step cluster method used to split the data and Bayesian phylogenetic tree analysis method applied to precisely construct phylogenetic tree for each clusters. RESULTS: Tree topology indicated that H3 strains evolved along a single evolution trunk and tree pattern and model parameter showed obvious variety tendency with time period. However, no geographic distribution features were found for key variation strains and big branch in trees. CONCLUSION: The evolution of human H3 strains were mainly driven by the interaction of human immune barriers and antigenic drift of virus. Since the influenza subtype had already been spread in human population, south China should not be considered as the originated areas of new strains, hence it should be treated as equally as other places in the world.

Antigens↗
PubMed2005

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

PURPOSE: Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated. METHODS: SAP fields were used that were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Dublin, CA) from 189 normal eyes and 156 eyes with glaucomatous optic neuropathy (GON) determined by masked review with stereoscopic optic disc photographs. The variational Bayesian independent component analysis mixture model (vB-ICA-mm) partitioned the SAP fields into the most informative number of clusters. Simultaneously, the model learned an optimal number of maximally independent axes for each cluster. RESULTS: The most informative number of clusters in the SAP set was two. vB-ICA-mm placed 68.6% of the eyes with GON in a cluster labeled G and 98.4% of the eyes with normal optic discs in a cluster labeled N. Cluster G optimally contained six axes. Post hoc analysis of patterns generated at -1 SD and +2 SD from the cluster G mean on the six axes revealed defects similar to those identified by experts as indicative of glaucoma. SAP fields associated with an axis showed increasing severity, as they were located farther in the positive direction from the cluster G mean. CONCLUSIONS: vB-ICA-mm represented the SAP fields with patterns that were meaningful for glaucoma experts. This process also captured severity in the patterns uncovered. These findings should validate vB-ICA-mm as a data-mining technique for new and unfamiliar complex tests.

Artificial Intelligence↗