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Delineating relative homogeneous G+C domains in DNA sequences.

The concept of homogeneity of G+C content is always relative and subjective. This point is emphasized and quantified in this paper using a simple example of one sequence segmented into two subsequences. Whether the sequence is homogeneous or not can be answered by whether the two-subsequence model describes the DNA sequence better than the one-sequence model. There are at least three equivalent ways of looking at the 1-to-2 segmentation: Jensen-Shannon divergence measure, log likelihood ratio test, and model selection using Bayesian information criterion. Once a criterion is chosen, a DNA sequence can be recursively segmented into multiple domains. We use one subjective criterion called segmentation strength based on the Bayesian information criterion. Whether or not a sequence is homogeneous and how many domains it has depend on this criterion. We compare six different genome sequences (yeast S. cerevisiae chromosome III and IV, bacterium M. pneumoniae, human major histocompatibility complex sequence, longest contigs in human chromosome 21 and 22) by recursive segmentations at different strength criteria. Results by recursive segmentation confirm that yeast chromosome IV is more homogeneous than yeast chromosome III, human chromosome 21 is more homogeneous than human chromosome 22, and bacterial genomes may not be homogeneous due to short segments with distinct base compositions. The recursive segmentation also provides a quantitative criterion for identifying isochores in human sequences. Some features of our recursive segmentation, such as the possibility of delineating domain borders accurately, are superior to those of the moving-window approach commonly used in such analyses.

Base Composition↗

Automated analysis of the American College of Radiology mammographic accreditation phantom images.

A significant metric in federal mammography quality standards is the phantom image quality assessment. The present work seeks to demonstrate that automated image analyses for American College of Radiology (ACR) mammographic accreditation phantom (MAP) images may be performed by a computer with objectivity, once a human acceptance level has been established. Twelve MAP images were generated with different x-ray techniques and digitized. Nineteen medical physicists in diagnostic roles (five of which were specially trained in mammography) viewed the original film images under similar conditions and provided individual scores for each test object (fibrils, microcalcifications, and nodules). Fourier domain template matching, used for low-level processing, combined with derivative filters, for intermediate-level processing, provided translation and rotation-independent localization of the test objects in the MAP images. The visibility classification decision was modeled by a Bayesian classifer using threshold contrast. The 50% visibility contrast threshold established by the trained observers' responses were: fibrils 1.010, microcalcifications 1.156, and nodules 1.016. Using these values as an estimate of human observer performance and given the automated localization of test objects, six images were graded with the computer algorithm. In all but one instance, the algorithm scored the images the same as the diagnostic physicists. In the case where it did not, the margin of disagreement was 10% due to the fact that the human scoring did not allow for half-visible fibrils (agreement occurred for the other test objects). The implication from this is that an operator-independent, machine-based scoring of MAP images is feasible and could be used as a tool to help eliminate the effect of observer variability within the current system, given proper, consistent digitization is performed.

Accreditation↗

Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network.

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.

Alkyl and Aryl Transferases↗

Adequacy of a vancomycin dosing regimen in patients receiving high-flux hemodialysis.

BACKGROUND: Some investigators have recommended the convenient practice of administering vancomycin doses during the last hour of the hemodialysis treatment. Accepting that a greater amount of vancomycin is lost to dialysis with this recent approach, the objective of this study is to determine the pharmacokinetics of vancomycin and assess the adequacy of this dosing regimen in maintaining therapeutic predialysis concentrations. METHODS: A sampling of 22 consecutive patients administered intradialytic vancomycin, 1 g, intravenously (IV) and maintenance doses of 500 mg during the last hour of high-flux dialysis sessions was studied. A population-modeling program and Bayesian pharmacokinetic analysis were used to identify all global and unique pharmacokinetic parameters of interest based on measured vancomycin predialysis concentrations. RESULTS: For the 22 patients studied, this regimen achieved the targeted predialysis concentration range of 5 to 20 microg/mL for 96% of levels, whereas more narrowly within 5 to 15 microg/mL for 86% of levels. Average amount of vancomycin removed during a standardized 3- to 4-hour dialytic session ranged from 30% +/- 7% to 38% +/- 8%. Average elimination half-life of vancomycin on hemodialysis treatment was 5.4 hours (interquartile range, 5.0 to 5.9 hours). Patients showed an average predialysis plasma concentration of 11 +/- 3 microg/mL for the first 7 days of therapy. CONCLUSION: Our results indicate that intradialytic dosing with vancomycin using a 1-g IV load and 500 mg IV with subsequent high-flux dialysis sessions conveniently maintains adequate predialysis plasma concentrations. The lack of drug accumulation with this regimen provides convincing support for a limited blood sampling approach to plasma concentration determinations.

Adult↗

Somatic cell count distributions during lactation predict clinical mastitis.

This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n = 1444) and two containing cow SCC (n = 933 and 11,825). Clinical mastitis was defined as a binary outcome, present or absent, for each lactation, and SCC were log (base 10) transformed. A generalized linear mixed model within a Bayesian framework was used for analysis. Parameters were estimated using Markov Chain Monte Carlo with Gibbs sampling. Results from the 3 data sets were similar. Increased maximum and standard deviation log SCC during lactation, rather than increased geometric mean, were the best overall indicators of clinical mastitis. Distributions of SCC were also investigated separately for different mastitis pathogens. Increased maximum log SCC was associated with clinical mastitis caused by all pathogen types. Increased standard deviation log SCC was associated with Staphylococcus aureus, and Streptococcus uberis clinical mastitis and increased coefficient of variation log SCC (standard deviation divided by mean) was associated with Escherichia coli clinical mastitis. Increased geometric mean lactation SCC was associated with an increased risk of Staph. aureus clinical mastitis but a reduced risk of E. coli clinical mastitis. Our results suggest that using measures of variation and maximum cow SCC would enhance the accuracy of predicting clinical mastitis, compared with geometric mean SCC, and therefore improve genetic programs that aim to select for clinical mastitis resistance. The results are also consistent with low SCC increasing susceptibility to some mastitis pathogens.

Animals↗

Genetic evaluation of the ratio of calf weaning weight to cow weight.

The phenotypic ratio of a calf's weaning weight to its dam's weight is thought to be an indicator of efficiency of the cow. Thus, the objectives of this research were to 1) estimate genetic parameters for the ratio of 200-d calf weight to mature-equivalent cow weight at weaning, its components, and other growth traits; and 2) evaluate responses to selection based on the ratio. Phenotypes evaluated were the ratio (100 kg/ kg; n = 4,184), birth weight (kg; n = 5,083), 200-d weight (kg; n = 4,902), 365-d weight (kg; n = 4,626), and mature-equivalent cow weight at weaning (kg; n = 4,375). In 1989, a randomly selected and mated control line and a line selected for greater values of the ratio were established. Average generation intervals were 3.39 +/- 0.05 and 3.90 +/- 0.08 yr in the ratio selected line and control line, respectively. The ratio selection line (n = 895) accumulated approximately 4.7 SD more selection differential than the control line (n = 912) over 2.5 generations. Data were analyzed with a multiple-trait Gibbs sampler for animal models to make Bayesian inferences. Heritability estimates (posterior mean +/- SD) for direct effects were 0.20 +/- 0.03, 0.46 +/- 0.04, 0.48 +/- 0.03, 0.58 +/- 0.04, and 0.76 +/- 0.02 for ratio, birth weight, 200-d weight, 365-d weight, and cow weight, respectively. Estimates for heritability of maternal effects were 0.58 +/- 0.05, 0.10 +/- 0.02, 0.13 +/- 0.02, and 0.10 +/- 0.02 for ratio, birth weight, 200-d weight, 365-d weight, respectively. Significant response to selection was limited to maternal effects: 1.32 +/- 0.38 ratio units per generation. As the ratio was a trait of the calf, estimated maternal genetic effects on the ratio contained both genetic effects due to dams that environmentally affected progeny performance and direct effects on the reciprocal of cow weight. In the control line, genetic trends in direct and maternal 200-d weight were -1.28 +/- 0.91 and 0.62 +/- 0.92 kg/generation, respectively, and the genetic trend in direct effects on cow weight was -5.72 +/- 2.80 kg/ generation. In the selection line, genetic trends in direct and maternal 200-d weight were 1.43 +/- 0.79 and 2.90 +/- 0.80 kg/generation and the genetic trend in cow weight was -2.79 +/- 2.43 kg/generation. Significant correlated responses were observed in direct effects on birth weight and maternal effects on 365-d weight. Results contraindicate use of the ratio of calf weaning weight to cow weight as a selection criterion.

Animal Husbandry↗

Genetic parameters for various random regression models to describe the weight data of pigs.

Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random regression model fits best to weight data of pigs. Two random regression models that described weight of individual pigs, one using orthogonal polynomials, and the other using splines, were compared. A comparison with a multivariate model, Akaike's information criterion, and the Bayesian-Schwarz information criterion were used to select the best model. Genetic, permanent environmental, and total variances increased with age. Heritabilities for the multivariate model ranged from 0.14 to 0.19, and for both random regression models the heritabilities were fluctuating around 0.17. Both genetic and phenotypic correlations decreased when the interval between measurements increased. The spline model needed fewer parameters than the multivariate and polynomial models. Akaike's information criterion was least for the spline model and greatest for the multivariate model. The Bayesian-Schwarz information criterion was least for the polynomial model and greatest for the multivariate model. Residuals of all models were normally distributed. Based on these results, it is concluded that random regression models provide the best fit to pig weight data.

Age Factors↗

Bayesian back-calculation using a multi-state model with application to HIV.

Back-calculation is a method of obtaining estimates of the number of infections of a disease over time. Data on an endpoint of the disease, together with knowledge of the time from infection to endpoint, allows reconstruction of the incidence of infection. The technique has had much success when applied to the HIV epidemic, using incidence of AIDS diagnoses to inform past HIV infections. In recent years, the period from infection to AIDS has changed considerably due to new regimes of anti-viral therapies. This has led to attempts to use incidence of first positive HIV test as an alternative basis for back-calculation. Developing on earlier work, this paper explores the feasibility of a multi-state formulation of the back-calculation method that models the disease and diagnosis processes and uses HIV diagnoses as an endpoint. Estimation is carried out in a Bayesian framework, which naturally allows incorporation of external information to inform the diagnosis probabilities. The idea is illustrated on data from the HIV epidemic in homosexuals in England and Wales.

Bayes Theorem↗

Colorectal cancer mass-screening: estimation of faecal occult blood test sensitivity, taking into account cancer mean sojourn time.

Mass screening using the faecal occult blood test (FOBT) can reduce mortality from colorectal cancer. Reliable estimation of FOBT sensitivity is crucial in assessing the potential effectiveness of a mass-screening procedure. Available estimates could be inaccurate because they neglect the temporal aspect of screening. The aim of our study was to estimate the sensitivity of the FOBT in mass screening for colorectal cancer, taking into account the duration of the pre-clinical phase of the disease assessed by the mean sojourn time (MST), and to assess whether MST and FOBT sensitivity differ according to cancer subsite. We analysed data taken from the first round of the mass-screening programme of the department of Calvados (France), involving 164,364 subjects of whom 43.4% participated in FOB screening. MST and sensitivity were estimated using a simple empirical approach, a traditional maximum likelihood method and log-linear modelling using the Bayesian technique of Gibbs sampling. MST was estimated as between 4.5 and 5 years for all subsites combined. According to the Gibbs sampling method, MSTs were 3.5, 6.4 and 2.6 years for proximal colon, distal colon and rectal cancer, respectively. Our estimation methods give a low sensitivity for the FOBT (50%), results for different subsites being closer to each other, slightly higher for proximal cancer. Our results strongly suggest that tumour growth rates are very different according to subsite, slowest for distal cancer and speediest for rectal cancer. Consideration of FOBT sensitivity without MST appears unreliable. Our results by subsite suggest that combining FOBT and sigmoidoscopy could be a good strategy for colorectal cancer screening.

Aged↗

Meta-analysis for the evaluation of potential surrogate markers.

We describe a meta-analysis approach for the evaluation of a potential surrogate marker. Surrogate markers are useful in helping to identify therapeutic mechanisms of action and disease pathogenesis, and for selecting therapies to take forward from phase II to phase III clinical trials. They have also become increasingly important for regulatory purposes by providing a basis for preliminary approval of drugs pending clinical outcome studies. Methodology for evaluating surrogate markers has focused on determining the difference in the effects of two treatments on clinical outcome in an individual clinical trial, and then estimating the proportion of this difference explained by the treatment's effects on the potential marker. Studies are, however, frequently underpowered or cease before they accumulate sufficient evidence to draw strong conclusions about the value of a potential surrogate marker using this approach, and there are also some technical difficulties with the approach. Consideration of the association between the difference in treatment effects on the clinical outcome and the difference in treatment effects on the potential marker over a range of trials provides an alternative means to evaluate a potential marker. We describe a meta-analysis approach using Bayesian methods to model this association. Importantly, this approach enables one to obtain prediction intervals for the true difference in clinical outcome for a given estimated treatment difference in the effect on the potential marker. We illustrate the methodology by applying it to results from studies of the AIDS Clinical Trials Group to assess the value of CD4 T-lymphocyte cell count as a potential surrogate marker for the treatment effects on the development of AIDS or death.

Anti-HIV Agents↗

Approximate Bayesian inference for random effects meta-analysis.

Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formulae that provide an efficient tool for a qualitative analysis and a quick numerical estimation of posterior quantities. They are shown to lead to sensible approximations in two examples of meta-analyses and to be in broad agreement with the more computationally intensive Gibbs sampling.

Antibiotic Prophylaxis↗

A comparison of various estimators of a treatment difference for a multi-centre clinical trial.

When a clinical trial is conducted at more than one centre it is likely that the true treatment effect will not be identical at each centre. In other words there will be some degree of treatment-by-centre interaction. A number of alternative approaches for dealing with this have been suggested in the literature. These include frequentist approaches with a fixed or random effects model for the observed data and Bayesian approaches. In the fixed effects model, there are two common competing estimators of the treatment difference, based on weighted or unweighted estimates from individual centres. Which one of these should be used is the subject of some controversy and we do not intend to take a particular methodological position in this paper. Our intention is to provide some insight into the relative merits of the indicated range of possible estimators of the treatment effect. For the fixed effects model, we also look at the merits of using a preliminary test for interaction assuming a 10 per cent significance level for the test. In order to make comparisons we have simulated a 'typical' trial which compares an active drug with a placebo in the treatment of hypertension, using systolic blood pressure as the primary variable. As well as allowing the treatment effect to vary between centres, we have concentrated on the particular case where one centre is out of line with the others in terms of its true treatment difference. The various estimators that result from the different approaches are compared in terms of mean squared error and power to reject the null hypothesis of no treatment difference. Overall, the approach that uses the fixed effects weighted estimator of overall treatment difference is recommended as one that has much to offer.

Analysis of Variance↗

Probabilistic small area risk assessment using GIS-based data: a case study on Finnish childhood diabetes. Geographic information systems.

A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15-year-olds in Finland. The model exploits aggregated pixel-wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk over the years. The model is applied in the construction of disease maps (aggregated 100 km(2) pixels), and spatial posterior predictive distributions are computed to study whether there can be found a statistically exceptional number of cases in a small area of interest.

Bayes Theorem↗

"Mental imbalance" and the prediction of recurrent delinquent behavior.

Cognitive and personality patterns of 84 court-referred adolescents were examined to identify predictors of recurrent delinquent behavior. Continued behavioral problems at follow-up were more likely in adolescents with discrepancies between Verbal and Performance IQ or large differences between "neurotic" and "psychotic" scale elevations on the MMPI. Positive outcomes were most likely for adolescents who could be described as "mildly neurotic." Combining the discrepancy scores from the intelligence and personality tests with other background variables in a Bayesian conditional probability model resulted in accurate predictions of later behavior for 81% of the sample. These findings suggest that imbalances in cognitive and personality development may limit a delinquent adolescent's ability to interact appropriately with the environment.

Adaptation, Psychological↗

Estimating treatment effects in randomized clinical trials with non-compliance: the impact of maternal smoking on birthweight.

This paper assesses the causal impact of late-term (8th month) maternal smoking on birthweight using data from a randomized clinical trial, in which some women were encouraged not to smoke, while others were not. The estimation of treatment effects in this case is made difficult as a result of the presence of non-compliers, women who would not change their smoking status, regardless of the receipt of encouragement. Because these women are not at risk of changing treatment status, treatment effect distributions may be difficult to construct for them. Consequently, the paper focuses on obtaining the distribution of treatment impacts for the sub-set of compliers found in the data. Because compliance status is not observed for all subjects in the sample, a Bayesian finite mixture model is estimated that recovers the treatment effect parameters of interest. The complier average treatment effect implies that smokers give birth to infants weighing 348 g less than those of non-smokers, on average, although the 95% posterior density interval contains zero. The treatment effect is stronger for women who were moderate smokers prior to pregnancy, implying a birthweight difference of 430 g. However, the model predicts that only about 22% of the women in the sample were at risk of changing their smoking behaviour in response to encouragement to quit.

Bayes Theorem↗

31P NMR Bayesian spectral analysis of rat brain in vivo.

Bayesian spectrum analysis for parameter estimation is a rigorous statistical (non-Fourier-based) method. Herein the Bayesian quadrature NMR model is introduced and applied to analysis of 31P NMR time domain data from in vivo rat brain. Immunity to both the brain spectrum "baseline hump" and the phase twist is demonstrated.

Animals↗

What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.

OBJECTIVES: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections. BACKGROUND: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions. METHODS: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes. RESULTS: Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used. CONCLUSIONS: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.

Bayes Theorem↗

A Bayesian approach to prospective binary outcome studies with misclassification in a binary risk factor.

Misclassification in a binary exposure variable within an unmatched prospective study may lead to a biased estimate of the disease-exposure relationship. It usually gives falsely small credible intervals because uncertainty in the recorded exposure is not taken into account. When there are several other perfectly measured covariates, interrelationships may introduce further potential for bias. Bayesian methods are proposed for analysing binary outcome studies in which an exposure variable is sometimes misclassified, but its correct values have been validated for a random subsample of the subjects. This Bayesian approach can model relationships between explanatory variables and between exploratory variables and the probabilities of misclassification. Three logistic regressions are used to relate disease to true exposure, misclassified exposure to true exposure and true exposure to other covariates. Credible intervals may be used to make decisions about whether certain parameters are unnecessary and hence whether the model can be reduced in complexity. In the disease-exposure model, for parameters representing coefficients related to perfectly measured covariates, the precision of posterior estimates is only slightly lower than would be found from data with no misclassification. For the risk factor which has misclassification, the estimates of model coefficients obtained are much less biased than those with misclassification ignored.

Bayes Theorem↗