Disease relevance of Bayes Theorem

• Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory [1].
• Maximum a posteriori Bayesian analysis has been shown to provide more accurate and precise estimates of the pharmacokinetic parameters of ibuprofen in children with CF, and may also be a useful tool to further investigate the relationship between measures of drug exposure and efficacy/toxicity outcomes [2].
• Diagnostic value of pleural fluid adenosine deaminase in tuberculous pleuritis with reference to HIV coinfection and a Bayesian analysis [3].
• This article describes the use of a combination of extended-connectivity fingerprints (ECFPs) and Laplacian-modified Bayesian analysis in a study of the inhibition of Escherichia coli dihydrofolate reductase [4].
• Diagnostic value of SCC, CEA and CYFRA 21.1 in lung cancer: a Bayesian analysis [5].

High impact information on Bayes Theorem

• Bayesian analysis and the GUSTO trial. Global Utilization of Streptokinase and Tissue Plasminogen Activator in Occluded Arteries [6].
• According to Bayes' theorem, and taking into account the false-positive rates of the tests in the population studied and the prevalence of MS and the true-positive rates for the tests as derived from published reports, abnormalities of VER and CSF protein together gave a probability of MS of 100% [7].
• We present probability graphs derived from Bayes' theorem, which show that for certain patients the B27 test, when used properly, is of clinical value as an aid to diagnosis [8].
• Here, we perform a Bayesian analysis of mitochondrial and Y chromosome diversity in three matrilocal and three patrilocal groups from northern Thailand to infer the number of males and females arriving in these populations each generation and to estimate the age of their range expansion [9].
• Bayesian analysis of linkage in the ATM IVS10-6T-->G-positive families showed an overall posterior probability of causality for this mutation of 0.008 [10].

Chemical compound and disease context of Bayes Theorem

• Application of the Bayes' theorem showed that in SSS the probability to develop atrial fibrillation is 33% for V1 < 110 msec and is for V1 < 90 msec still higher than that reported in DDD paced patients [11].

Biological context of Bayes Theorem

• Using this computed a priori risk of AD and published studies of APOE genotypes in individuals with and without AD, we used a Bayesian analysis to determine the risk of developing AD, with and without an APOE* epsilon 4 allele, for unaffected 65-year-olds [12].
• These parameters were used as reference population values to estimate the pharmacokinetics of amikacin in a second group of 20 patients by the Bayesian method, with 2 blood samples per patient [13].

Anatomical context of Bayes Theorem

• METHODS: We identified 323 patients undergoing thallium-201 SPECT myocardial perfusion imaging who either had < 5% probability of coronary artery disease (CAD) by Bayesian analysis or who underwent cardiac catheterization within 60 days of stress testing [14].
• Bayesian analysis showed probabilities of 0.05 that metformin reduces FSH requirement by at least 10%, and of 0.61 that at least 10% more oocytes are collected after metformin co-treatment [15].

Associations of Bayes Theorem with chemical compounds

• CONCLUSION: The applicability of a Bayesian method for the prediction of the carboplatin exposure by use of one or two samples without the necessity for exact timing of infusion duration and sampling was demonstrated [16].
• The pharmacokinetic parameters of phenytoin in individual patients were estimated by means of empirical bayesian analysis, in which the prior information was the population parameters for Japanese patients with epilepsy [17].
• Data from these experiments performed on glutamine-binding protein are analyzed statistically using Bayes' theorem to yield objective probability scoring functions for matching chemical shifts [18].
• Sources of plasma glucose by automated Bayesian analysis of 2H NMR spectra [19].
• Individualization of theophylline dosage: a Bayesian method evaluated in patients with once-daily dosing [20].

Gene context of Bayes Theorem

• A Bayesian analysis of the influence of GSTT1 polymorphism on the cancer risk estimate for dichloromethane [21].
• Finally, applying Bayes' theorem again, we obtained a probability of paternity of 0.992 for the ACTBP2 locus alone [22].
• RESULTS: Bayesian analysis of the treatment effect for HER2 and other variables indicated a clinical benefit from CMF treatment in all subgroups defined according to variables status [23].
• In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method [24].
• The pretest likelihood for CAD was calculated from age and history; the post-test likelihood was calculated from Bayes' theorem and the average information content of several diagnostic methods was assessed in categorical and compartmental models [25].

Analytical, diagnostic and therapeutic context of Bayes Theorem

• Diagnostic value of the bronchial provocation test with methacholine in asthma. A Bayesian analysis approach [26].
• Evaluation of a Bayesian method of amikacin dosing in intensive care unit patients with normal or impaired renal function [27].
• The present study aims to determine the population pharmacokinetic parameters of cyclosporine (CsA) after multiple oral administration of the microemulsion formulation, Neoral, in kidney transplant patients and to propose a limited sampling strategy to predict AUC(0-4h) using them and the Bayesian method [28].
• The utility of Bayes' theorem in the noninvasive diagnosis of coronary artery disease (CAD) was analyzed in 147 patients who underwent electrocardiographic stress testing, thallium-201 perfusion imaging and coronary angiography [29].
• Measured by 95 per cent confidence interval (CI) coverage rate and root mean square error (RMSE) criteria, it was found that the Bayesian method using a non-informative prior outperformed both bootstrap and the Delta methods, particularly for small sample sizes [30].

References