Chain Monte Carlo Bayesian Analysis Research Articles

The GeMS/GSAOI Galactic Globular Cluster Survey (G4CS). II. Characterization of 47 Tuc with Bayesian Statistics

We present a photometric analysis of globular cluster 47 Tuc (NGC 104) using near-IR imaging data from the GeMS/GSAOI Galactic Globular Cluster Survey (G4CS), which is in operation at Gemini-South telescope. Our survey is designed to obtain AO-assisted deep imaging with near diffraction-limited spatial resolution of the central fields of Milky Way globular clusters. The G4CS near-IR photometry was combined with an optical photometry catalog that was obtained from Hubble Space Telescope survey data to produce a high-quality color–magnitude diagram that reaches down to K s ≈ 21 Vega mag. We used the software suite BASE-9, which uses an adaptive Metropolis sampling algorithm to perform a Markov chain Monte Carlo Bayesian analysis, and obtained probability distributions and precise estimates for the age, distance, and extinction cluster parameters. Our best estimate for the age of 47 Tuc is ± 0.08 Gyr and our true distance modulus estimate is (m−M)0 = 13.250 ± 0.028 mag, which are in tight agreement with previous studies using Gaia DR2 parallax and detached eclipsing binaries.

Tests of general relativity with stellar-mass black hole binaries observed by LISA

We consider the observation of stellar-mass black holes binaries with the Laser Interferometer Space Antenna (LISA). Preliminary results based on Fisher information matrix analyses have suggested that gravitational waves from those sources could be very sensitive to possible deviations from the theory of general relativity and from the strong equivalence principle during the low-frequency binary inspiral. We perform a full Markov Chain Monte Carlo Bayesian analysis to quantify the sensitivity of these signals to two phenomenological modifications of general relativity, namely a putative gravitational dipole emission and a non-zero mass for the graviton, properly accounting for the detector's response. Moreover, we consider a scenario where those sources could be observed also with Earth-based detectors, which should measure the coalescence time with precision better than $1 \ {\rm ms}$. This constraint on the coalescence time further improves the bounds that we can set on those phenomenological deviations from general relativity. We show that tests of dipole radiation and the graviton's mass should improve respectively by seven and half an order(s) of magnitude over current bounds. Finally, we discuss under which conditions one may claim the detection of a modification to general relativity.

Prediction of treatment failure during infliximab induction therapy in inflammatory bowel disease patients based on pharmacokinetic and pharmacodynamic modeling

BackgroundIn infliximab (IFX) treatment for Crohn's disease (CD) and ulcerative colitis (UC), it is difficult to predict treatment failure during the induction phase. In the present study for optimal IFX treatment, we attempted to estimate serum IFX concentration and clinical response in individual patients during the induction phase to predict the indication of therapeutic effect and the possibility of treatment failure in the maintenance phase. MethodsWe estimated pharmacokinetic and pharmacodynamic (PK/PD) parameters and predicted the serum IFX concentration and clinical response using a PK/PD model and Markov chain Monte Carlo Bayesian analysis method during the induction phase. Then, we determined whether the indication of therapeutic effect between predicted and observed clinical response were matched during the maintenance phase. ResultsData obtained from 15 patients were analyzed. The correlation between predicted and observed values of serum IFX concentration (Pearson product-moment correlation coefficient, 0.700; P < 0.0001, n = 68) and clinical response of CD patients (0.790; P < 0.0001, n = 25) and UC patients (0.702; P = 0.0004, n = 21) were significantly high. The indication of therapeutic effect at the final time point of each patient (from day 115 to day 203) were successfully predicted in 14 of 15 patients (93.3%). ConclusionsThis study presents prediction of serum IFX concentration and clinical response in individual patients during induction therapy, with presumption of the indication of therapeutic effect and the treatment failure in the maintenance phase. Our results show the possibility of optimizing IFX therapy during the induction phase.

Population Pharmacokinetics of Pemetrexed in Adult Non-Small Cell Lung Cancer in Indian Patients.

The objective of the study was to develop a population pharmacokinetic model of pemetrexed and identify factors contributing to variability in exposure in Indian patients. Plasma samples were obtained from a cohort of 85 patients following 500 mg/m2 intravenous infusion and population pharmacokinetic analysis was performed using NONMEM (version 7.3.0). The stochastic approximation expectation maximization method was used to estimate parameters. The full covariate model approach was used by specifying clinically meaningful covariates a priori. Credible intervals obtained using Markov chain Monte Carlo Bayesian analysis were used to reduce the full covariate model by eliminating the covariates whose CI included the null. Model qualification was performed using visual predictive check and bootstrap. The final population parameter estimates and relative standard error for clearance (CL) was 3.3 L/h (10.8), central volume of distribution (V1) was 5.2 L (7.8), peripheral volume of distribution (V2) was 5.9 L (14.5) and intercompartmental clearance (Q) was 6.8 L/h (14.3). A large between-subject variability (50%-108% coefficient of variation) was observed in pharmacokinetic parameters. The percent coefficient of variation for the area under the plasma concentration-time curve from time zero to infinity was 72% and for maximum concentration was 68.25%. Diagnostic plots showed no major bias in the model. The final model included V1, V2, and Q scaled to body surface area raised to a fixed exponent of 1. Creatinine clearance and sex on clearance and albumin on V1 were statistically significant covariates based on Bayesian credible interval. However, traditional bootstrap resulted in a 95% confidence interval of the sex effect parameter including null. Given the size and nonsignificant sex effect in traditional bootstrap, it is considered clinically not significant.

Limits on semiclassical fluctuations in the primordial universe

We place limits on semiclassical fluctuations that might be present in the primordial perturbation spectrum. These can arise if some signatures ofpre-inflationary features survive the expansion, or could be created by whatever mechanism ends inflation. We study two possible models for such remnant fluctuations, both of which break the isotropy of CMB on large scales. We first consider a semiclassical fluctuation in one Fourier mode of primordial perturbations. The second scenario we analyze is a semiclassical Gaussian bump somewhere in space. These models are tested with the seven-year WMAP data using a Markov Chain Monte Carlo Bayesian analysis, and we place limits on these fluctuations. The upper bound for the amplitude of a fluctuation in a single Fourier mode is a ⩽ 10−4, while for the Gaussian bump a ⩽ 10−3.

Final report on CCQM-K80: Comparison of value-assigned CRMs and PT materials: Creatinine in human serum

The 2009 CCQM-K80 'Comparison of value-assigned CRMs and PT materials: creatinine in human serum' is the first in a series of key comparisons directly testing the chemical measurement services provided to customers by National Metrology Institutes (NMIs) and Designated Institutes. CCQM-K80 compared the assigned serum creatinine values of certified reference materials (CRMs) using measurements made on these materials under repeatability conditions. Six NMIs submitted 17 CRM materials for evaluation, all intended for sale to customers. These materials represent nearly all of the higher-order CRMs then available for this clinically important measurand.The certified creatinine mass fraction in the materials ranged from 3 mg/kg to 57 mg/kg. All materials were stored and prepared according the specifications provided by each NMI. Samples were processed and analyzed under repeatability conditions by one analyst using isotope dilution liquid chromatography–mass spectrometry. The instrumental repeatability imprecision, expressed as a percent relative standard deviation, was 1.2%.Given the number of materials and the time required for each analysis, the measurements were made in two measurement campaigns ('runs'). In both campaigns, replicate analyses (two injections of one preparation separated in time) were made on each of two or three independently prepared aliquots from one randomly selected unit of each of the 17 materials. The mean value, between-campaign, between-aliquot and between-replicate variance components, standard uncertainty of the mean value, and the number of degrees of freedom associated with the standard uncertainty were estimated using a linear mixed model. Since several of the uncertainties estimated using this traditional frequentist approach were associated with a single degree of freedom, Markov Chain Monte Carlo Bayesian analysis was used to estimate 95% level-of-confidence coverage intervals, U95. Uncertainty-weighted generalized distance regression was used to establish the key comparison reference function (KCRF) relating the assigned values to the repeatability measurements. Parametric bootstrap Monte Carlo was used to estimate 95% level-of-confidence coverage intervals for the degrees of equivalence of materials, d ± U95(d), and of the participating NMIs, D ± U95(D). Because of the wide range of creatinine mass fraction in the materials, these degrees of equivalence are expressed in percent relative form: %d ± U95(%d) and %D ± U95(%D).On the basis of leave-one-out cross-validation, the assigned values for 16 of the 17 materials were deemed equivalent at the 95% level of confidence. These materials were used to define the KCRF. The excluded material was identified as having a marginally underestimated assigned uncertainty, giving it large and potentially anomalous influence on the KCRF. However, this material's %d of 1.4 ± 1.5 indicates that it is equivalent with the other materials at the 95% level of confidence. The median |%d| for all 17 of the materials is 0.3 with a median U95(%d) of 1.9. All of these higher-order CRMs for creatinine in human serum are equivalent within their assigned uncertainties.The median |%D| for the participating NMIs is 0.3 with a median U95(%D) of 2.1. These results demonstrate that all participating NMIs have the ability to correctly value-assign CRMs and proficiency test materials for creatinine in human serum and similar measurands.Main text.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/.The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Final report on CCQM-K79: Comparison of value-assigned CRMs and PT materials: Ethanol in aqueous matrix

The 2010 CCQM-K79 'Comparison of value-assigned CRMs and PT materials: Ethanol in aqueous media' is the second key comparison directly testing the chemical measurement services provided to customers by National Metrology Institutes (NMIs) and Designated Institutes (DIs). CCQM-K79 compared the assigned ethanol values of proficiency test (PT) and certified reference materials (CRMs) using measurements made on these materials under repeatability conditions. Nine NMIs submitted 27 CRM or value-assigned PT materials for evaluation. These materials represent many of the higher-order reference materials then available for this commercially and forensically important measurand.The assigned ethanol mass fraction in the materials ranged from 0.1 mg/kg to 334 mg/kg. All materials were stored and prepared according the specifications provided by each NMI. Samples were processed and analyzed under repeatability conditions by one analytical team using a gas chromatography with flame ionization detection (GC-FID) method of demonstrated trueness and precision.Given the number of materials and the time required for each analysis, the majority of the measurements were made in two measurement campaigns ('runs'). Due to a shipping delay from one NMI, an unanticipated third campaign was required. In all three campaigns, replicate analyses (three injections of one preparation separated in time) were made for one randomly selected unit of each of the 27 materials. Nine of the 27 materials were gravimetrically diluted before measurement to provide solutions with ethanol mass fraction in the established linear range of the GC-FID method. The repeatability measurement value for each analyzed solution was estimated as the mean of all replicate values. The within- and between-campaign variance components were estimated using one-way ANOVA. Markov Chain Monte Carlo Bayesian analysis was used to estimate 95% level-of-confidence coverage intervals for the mean values.Uncertainty-weighted generalized distance regression was used to establish the key comparison reference function (KCRF) relating the assigned values to the repeatability measurements. On the basis of leave-one-out cross-validation, all of the assigned values for all 27 materials were deemed equivalent at the 95% level of confidence. These materials were used to define the KCRF.Parametric bootstrap Monte Carlo was used to estimate 95% level-of-confidence coverage intervals for the degrees of equivalence of materials, d ± U95(d), and of the participating NMIs, D ± U95(D). Because of the very wide range of ethanol mass fraction in the materials, these degrees of equivalence are expressed in percent relative form: %d ± U95(%d) and %D ± U95(%D). The median of the absolute values of the %D for the participating NMIs is less than 0.05% with a median U95(%D) of less than 1%. These results demonstrate that the participating NMIs have the ability to correctly value-assign CRMs and proficiency test materials for ethanol in aqueous media and similar measurands.Main text.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/.The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Markov Chain Monte Carlo Bayesian Analysis for Population Pharmacokinetics of Dasatinib in Japanese Adult Subjects with Chronic Myeloid Leukemia and Philadelphia Chromosome Positive Acute Lymphoblastic Leukemia

This study evaluated the population pharmacokinetics of dasatinib in Japanese subjects with imatinib-resistant or -intolerant chronic myeloid leukemia (CML) and Philadelphia chromosome positive acute lymphoblastic leukemia (Ph+ALL) enrolled in 3 Japanese clinical trials, and compared individual PK estimates to that of non-Japanese subjects in global database. A full Markov Chain Monte Carlo Bayesian analysis method in NONMEM 7 was utilized for the estimation of model parameters. In terms of covariate model selection, baseline demographic and clinical laboratory covariates were assessed on oral clearance (CL/F). The food effect and the dose effect were also tested on the relative bioavailability (FR) and CL/F, respectively. A total of 706 observations were obtained from 63 Japanese subjects who received twice daily administration of dasatinib at 50, 70 and 90 mg, and once daily administration at 100 mg. Consistent with a PPK model in non-Japanese subjects, plasma concentration-time data were well described by a linear two-compartment model with the inter-occasion variability (IOV) on the relative bioavailability (FR), which is to account for between-visit difference of dasatinib exposure within a subject apparently observed in a phase 1/2 study in Japanese subjects. Comparable exposures of individual Japanese subjects with that of non-Japanese subjects were obtained. Investigation of covariates revealed neither marked trends nor clinically relevant effect on CL/F or FR. These results indicated that no dose adjustment is warranted for Japanese CML and Ph+ALL patients, based on their body size, age, or gender.

METHODS USED TO CALCULATE DOSES RESULTING FROM INHALATION OF CAPSTONE DEPLETED URANIUM AEROSOLS

The methods used to calculate radiological and toxicological doses to hypothetical persons inside either a U.S. Army Abrams tank or Bradley Fighting Vehicle that has been perforated by depleted uranium munitions are described. Data from time- and particle-size-resolved measurements of depleted uranium aerosol as well as particle-size-resolved measurements of aerosol solubility in lung fluids for aerosol produced in the breathing zones of the hypothetical occupants were used. The aerosol was approximated as a mixture of nine monodisperse (single particle size) components corresponding to particle size increments measured by the eight stages plus the backup filter of the cascade impactors used. A Markov Chain Monte Carlo Bayesian analysis technique was employed, which straightforwardly calculates the uncertainties in doses. Extensive quality control checking of the various computer codes used is described.

Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data

In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is proposed. The Bayesian framework is appealing since complex models can be adopted in the analysis both for the image and noise model. Here, the noise autocorrelation is taken into account by adopting an AutoRegressive model of order one and a versatile non-linear model is assumed for the task-related activation. Model parameters include the noise variance and autocorrelation, activation amplitudes and the hemodynamic response function parameters. These are estimated at each voxel from samples of the Posterior Distribution. Prior information is included by means of a 4D spatio-temporal model for the interaction between neighbouring voxels in space and time. The results show that this model can provide smooth estimates from low SNR data while important spatial structures in the data can be preserved. A simulation study is presented in which the accuracy and bias of the estimates are addressed. Furthermore, some results on convergence diagnostic of the adopted algorithm are presented. To validate the proposed approach a comparison of the results with those from a standard GLM analysis, spatial filtering techniques and a Variational Bayes approach is provided. This comparison shows that our approach outperforms the classical analysis and is consistent with other Bayesian techniques. This is investigated further by means of the Bayes Factors and the analysis of the residuals. The proposed approach applied to Blocked Design and Event Related datasets produced reliable maps of activation.