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How much quality control is enough? A cost-effectiveness model for clinical laboratory quality control procedures (illustrated by its application to a ligand-assay-based screening program).

Quality assurance testing represents a substantial proportion of the clinical laboratory budget, but current guidelines are based on criteria that pertain to analytic error rather than to optimization of the cost-effectiveness of patient care. A general Bayesian mathematical model for the cost-effectiveness of assay quality control has been developed, and is demonstrated using previously published data. The cost-effectiveness of quality assurance as defined here depends upon the prevalence of disease, the shapes of the distributions of test results observed in the non-diseased and diseased populations, the decision limit selected for labeling results positive or negative, the costs and benefits associated with each of the possible therapeutic outcomes, the magnitude of random and systematic analytical errors, the statistical power of the quality control test in use, the costs associated with delays due to re-assay, and the proportion of total test cost attributable to quality control procedures. Given current clinical laboratory practice, much of this information will not be routinely available. The model combines these factors into a simple equation with three terms: one for the cost of the original and any required repeat laboratory analyses, one for the cost of delay entailed by the rejection of an assay batch, and one for the change in total costs consequent to rejection of erroneous assay results.

Clinical Laboratory Techniques↗
PubMed2004

Model-based assignment and inference of protein backbone Nuclear Magnetic Resonances.

Nuclear Magnetic Resonance (NMR) spectroscopy is a key experimental technique used to study protein structure, dynamics, and interactions. NMR methods face the bottleneck of spectral analysis, in particular determining the resonance assignments, which help define the mapping between atoms in the protein and peaks in the spectra. A substantial amount of noise in spectral data, along with ambiguities in interpretation, make this analysis a daunting task, and there exists no generally accepted measure of uncertainty associated with the resulting solutions. This paper develops a model-based inference approach that addresses the problem of characterizing uncertainty in backbone resonance assignment. We argue that NMR spectra are subject to random variation, and ignoring this stochasticity can lead to false optimism and erroneous conclusions. We propose a Bayesian statistical model that accounts for various sources of uncertainty and provides an automatable framework for inference. While assignment has previously been viewed as a deterministic optimization problem, we demonstrate the importance of considering all solutions consistent with the data, and develop an algorithm to search this space within our statistical framework. Our approach is able to characterize the uncertainty associated with backbone resonance assignment in several ways: 1) it quantifies of uncertainty in the individually assigned resonances in terms of their posterior standard deviations; 2) it assesses the information content in the data with a posterior distribution of plausible assignments; and 3) it provides a measure of the overall plausibility of assignments. We demonstrate the value of our approach in a study of experimental data from two proteins, Human Ubiquitin and Cold-shock protein A from E. coli. In addition, we provide simulations showing the impact of experimental conditions on uncertainty in the assignments.

Journal Article↗
PubMed2006

Genetic variation of Mycobacterium avium ssp. paratuberculosis infection in US Holsteins.

The objective of this study was to estimate genetic variability of Mycobacterium avium ssp. paratuberculosis infection in US Holsteins. Blood and fecal samples were collected primarily from daughters of 12 bulls in their second or third lactation. Routine disease testing of the sires documented that they were not infected. Herds without a "suspect" or positive ELISA (sample/positive ratio > or = 0.10) or positive fecal culture test were deleted from the data set. The remaining 4,603 cows from 238 herds and 46 sires were used to estimate heritability of M. paratuberculosis infection. Heritability was estimated with 3 Johne's disease diagnostic tests: 1) fecal culture alone, 2) serum antibody ELISA alone, and 3) both tests (combined) with a positive animal defined as all animals with either a positive fecal culture or ELISA test. Four statistical models were used to estimate heritability: 1) linear (ELISA), 2) threshold (fecal culture and combined), 3) ordered threshold (ELISA), and 4) bivariate linear-threshold (ELISA-fecal culture). A sire model and Bayesian approach using Markov chain Monte Carlo methods were used in each case. Heritability of infection based on the fecal culture test was 0.153 [posterior standard deviation (PSD) = 0.115]. Heritability with the ELISA was 0.159 (PSD = 0.090) with a linear model and 0.091 (PSD = 0.053) with an ordered threshold model. Heritability of the combined tests was 0.102 (PSD = 0.066). Heritability estimates of fecal culture and ELISA with the bivariate model varied slightly from estimates obtained with the univariate models (0.125 and 0.183, respectively), with a corresponding increase in precision (PSD = 0.096 and 0.082, respectively). This study demonstrates that exploitable genetic variation exists in dairy cattle for M. paratuberculosis infection susceptibility.

Animals↗
PubMed2004

Evolution of human immunodeficiency virus type 1 coreceptor usage during antiretroviral Therapy: a Bayesian approach.

There is substantial evidence for ongoing replication and evolution of human immunodeficiency virus type 1 (HIV-1), even in individuals receiving highly active antiretroviral therapy. Viral evolution in the presence of antiviral therapy needs to be considered when developing new therapeutic strategies. Phylogenetic analyses of HIV-1 sequences can be used for this purpose but may give rise to misleading results if rates of intrapatient evolution differ significantly. To improve analyses of HIV-1 evolution relevant to studies of pathogenesis and treatment, we developed a Bayesian hierarchical model that incorporates all available sequence data while simultaneously allowing the phylogenetic parameters of each patient to vary. We used this method to examine evolutionary changes in HIV-1 coreceptor usage in response to treatment. We examined patients whose viral populations exhibited a shift in coreceptor utilization in response to therapy. CXCR4 (X4) strains emerged in each patient but were suppressed following initiation of new antiretroviral regimens, so that CCR5-utilizing (R5) strains predominated. By phylogenetically reconstructing the evolutionary relationship of HIV-1 obtained longitudinally from each patient, it was possible to examine the origin of the reemergent R5 virus. Using our Bayesian hierarchical approach, we found that the reemergent R5 virus detectable after therapy was more closely related to the predecessor R5 virus than to the X4 strains. The Bayesian hierarchical approach, unlike more traditional methods, makes it possible to evaluate competing hypotheses across patients. This model is not limited to analyses of HIV-1 but can be used to elucidate evolutionary processes for other organisms as well.

Antiretroviral Therapy, Highly Active↗
PubMed2006

Speciation in reverse: morphological and genetic evidence of the collapse of a three-spined stickleback (Gasterosteus aculeatus) species pair.

Historically, six small lakes in southwestern British Columbia each contained a sympatric species pair of three-spined sticklebacks (Gasterosteus aculeatus). These pairs consisted of a 'benthic' and 'limnetic' species that had arisen postglacially and, in four of the lakes, independently. Sympatric sticklebacks are considered biological species because they are morphologically, ecologically and genetically distinct and because they are strongly reproductively isolated from one another. The restricted range of the species pairs places them at risk of extinction, and one of the pairs has gone extinct after the introduction of an exotic catfish. In another lake, Enos Lake, southeastern Vancouver Island, an earlier report suggested that its species pair is at risk from elevated levels of hybridization. We conducted a detailed morphological analysis, as well as genetic analysis of variation at five microsatellite loci for samples spanning a time frame of 1977 to 2002 to test the hypothesis that the pair in Enos Lake is collapsing into a hybrid swarm. Our morphological analysis showed a clear breakdown between benthics and limnetics. Bayesian model-based clustering indicated that two morphological clusters were evident in 1977 and 1988, which were replaced by 1997 by a single highly variable cluster. The most recent 2000 and 2002 samples confirm the breakdown. Microsatellite analysis corroborated the morphological results. Bayesian analyses of population structure in a sample collected in 1994 indicated two genetically distinct populations in Enos Lake, but only a single genetic population was evident in 1997, 2000, and 2002. In addition, genetic analyses of samples collected in 1997, 2000, and 2002 showed strong signals of 'hybrids'; they were genetically intermediate to parental genotypes. Our results support the idea that the Enos Lake species pair is collapsing into a hybrid swarm. Although the precise mechanism(s) responsible for elevated hybridization in the lake is unknown, the demise of the Enos Lake species pair follows the appearance of an exotic crayfish, Pascifasticus lenisculus, in the early 1990s.

Animals↗
PubMed2001

Comparison of two methods to obtain a desired first isepamicin peak in intensive care patients.

A randomized multicenter study in intensive care unit (ICU) patients, evaluated the capacity of a Bayesian method to obtain an optimal first isepamicin (ISP) peak of 80 mg/L in comparison to a fixed loading dose (LD). Patients (n=236) over 18 years of age were enrolled from 6 September 1997 to 17 July 1999 and randomly assigned to received ISP in a calculated dose (CD) or a loading dose (LD) of 25 mg/kg body weight. The CD was estimated using a specific population model with Bayesian methodology implemented in the PKS program (Abbott PKS, Abbott Diagnostics, Rungis, France). The data required included age, body weight, height, gender and serum creatinine. ISP disposition is described by a one-compartment model. Blood samples were drawn 1 and 24 h after the start of infusion for fluorescence polarization immunoassay measurement of serum ISP concentrations. The predictive performance was assessed by computing bias and precision. Peak concentrations were significantly higher in CD group than the LD group (84.2 +/- 28.6 vs. 74.7 +/- 24.1 mg/L, respectively; P=0.008), but trough levels were comparable. The optimal ISP peak was attained by a significantly higher percentage of CD patients (P=0.018), and by significantly more CD patients on mechanical ventilation (P=0.025), and with simplified acute physiological scores (SAPS) > 35 (P=0.002). Pharmacokinetic parameters were similar for the two groups with large interindividual variations. Mean (+/- SD) volume of distribution of ventilated patients (72%) was significantly higher than of nonventilated patients (23.31 +/- 7.35 vs. 20.60 +/- 6.30 L, respectively; P=0.001). No relationship was found between the volume of distribution and SAPS. Total clearance was significantly correlated with estimated CLCR (creatinine clearance) (P=0.0001). Precision (RMSE) is better for CD than for LD strategy, respectively 27.96 and 28.66 mg/L. The Bayesian method was significantly more accurate and performed particularly well in ventilated patients and patients with high SAPS, compare to an LD of 25 mg/kg to obtain a first ISP peak of 80 mg/L in ICU patients. Therefore, a fixed dose of 28.5 mg/kg would be also adequate to reach a peak of 80 mg/L.

Acute Disease↗
PubMed2006

Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania.

OBJECTIVE: To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. METHODS: Bayesian geostatistical models were developed using parasitological data from 143 schools. RESULTS: In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. CONCLUSION: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.

Adolescent↗
PubMed2000

Back-calculating the age-specific incidence of recurrent subclinical Haemophilus influenzae type b infection.

We consider the estimation of an age-specific incidence rate of a subclinical Haemophilus influenzae type b (Hib) infection from data recording the ages of children with a clinical Hib infection (Hib disease). The model is based on the assumption that the probability of being immune to clinical infection is determined by the time of the previous immunization caused by a subclinical infection, and by the distribution of the duration of immunity. We use a non-parametric Bayesian intensity model to arrive at smooth estimates of incidence rates. The estimated age-specific incidence rate of subclinical Hib infection is almost constant which indicates that the observed age-specific pattern of clinical Hib infection incidence is mainly due to immunity by either maternally derived antibodies or by immunizing subclinical infections. The estimated rate is relatively high, corresponding to one immunizing subclinical infection in less than two years.

Age Distribution↗
PubMed2007

Performance assessment for radiologists interpreting screening mammography.

When interpreting screening mammograms radiologists decide whether suspicious abnormalities exist that warrant the recall of the patient for further testing. Previous work has found significant differences in interpretation among radiologists; their false-positive and false-negative rates have been shown to vary widely. Performance assessments of individual radiologists have been mandated by the U.S. government, but concern exists about the adequacy of current assessment techniques. We use hierarchical modelling techniques to infer about interpretive performance of individual radiologists in screening mammography. While doing this we account for differences due to patient mix and radiologist attributes (for instance, years of experience or interpretive volume). We model at the mammogram level, and then use these models to assess radiologist performance. Our approach is demonstrated with data from mammography registries and radiologist surveys. For each mammogram, the registries record whether or not the woman was found to have breast cancer within one year of the mammogram; this criterion is used to determine whether the recall decision was correct. We model the false-positive rate and the false-negative rate separately using logistic regression on patient risk factors and radiologist random effects. The radiologist random effects are, in turn, regressed on radiologist attributes such as the number of years in practice. Using these Bayesian hierarchical models we examine several radiologist performance metrics. The first is the difference between the false-positive or false-negative rate of a particular radiologist and that of a hypothetical 'standard' radiologist with the same attributes and the same patient mix. A second metric predicts the performance of each radiologist on hypothetical mammography exams with particular combinations of patient risk factors (which we characterize as 'typical', 'high-risk', or 'low-risk'). The second metric can be used to compare one radiologist to another, while the first metric addresses how the radiologist is performing compared to an appropriate standard. Interval estimates are given for the metrics, thereby addressing uncertainty. The particular novelty in our contribution is to estimate multiple performance rates (sensitivity and specificity). One can even estimate a continuum of performance rates such as a performance curve or ROC curve using our models and we describe how this may be done. In addition to assessing radiologists in the original data set, we also show how to infer about the performance of a new radiologist with new case mix, new outcome data, and new attributes without having to refit the model.

Adult↗
PubMed2006

Municipal distribution of bladder cancer mortality in Spain: possible role of mining and industry.

BACKGROUND: Spain shows the highest bladder cancer incidence rates in men among European countries. The most important risk factors are tobacco smoking and occupational exposure to a range of different chemical substances, such as aromatic amines. METHODS: This paper describes the municipal distribution of bladder cancer mortality and attempts to "adjust" this spatial pattern for the prevalence of smokers, using the autoregressive spatial model proposed by Besag, York and Molliè, with relative risk of lung cancer mortality as a surrogate. RESULTS: It has been possible to compile and ascertain the posterior distribution of relative risk for bladder cancer adjusted for lung cancer mortality, on the basis of a single Bayesian spatial model covering all of Spain's 8077 towns. Maps were plotted depicting smoothed relative risk (RR) estimates, and the distribution of the posterior probability of RR>1 by sex. Towns that registered the highest relative risks for both sexes were mostly located in the Provinces of Cadiz, Seville, Huelva, Barcelona and Almería. The highest-risk area in Barcelona Province corresponded to very specific municipal areas in the Bages district, e.g., Suría, Sallent, Balsareny, Manresa and Cardona. CONCLUSION: Mining/industrial pollution and the risk entailed in certain occupational exposures could in part be dictating the pattern of municipal bladder cancer mortality in Spain. Population exposure to arsenic is a matter that calls for attention. It would be of great interest if the relationship between the chemical quality of drinking water and the frequency of bladder cancer could be studied.

Adolescent↗
PubMed2003

A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface.

A hybrid probabilistic modeling approach that integrates artificial neural networks (ANNs) with statistical Bayesian conditional probability estimation is proposed. The suggested approach benefits from the power of ANNs as highly flexible nonlinear mapping paradigms, and the Bayes' theorem for computing probabilities of bacterial growth with the aid of Parzen's probability distribution function estimators derived for growth and no-growth (G/NG) states. The proposed modeling approach produces models that can predict the probability of growth of targeted microorganism as affected by a set of parameters pertaining to extrinsic factors and operating conditions. The models also can be used to define the probabilistic boundary (interface) between growth and no-growth, and as such can define and predict the values of critical parameters required to keep a desired pre-specified bacterial growth risk in check. A modular system incorporating the various computational modules was constructed to illustrate the application of the hybrid approach to the probabilistic modeling of growth of pathogenic Escherichia coli strain as affected by temperature and water activity. The proposed approach was compared to other techniques including the traditional linear and nonlinear logistic regression. Results indicated that the hybrid approach outperforms the other approaches in its accuracy as well as flexibility to extract the implicit interrelationships between the various parameters. Advantages and limitations of the approach were also discussed and compared to those of other techniques.

Bayes Theorem↗
PubMed2005

Malaria mapping using transmission models: application to survey data from Mali.

Geographic mapping of the distribution of malaria is complicated by the limitations of the available data. The most widely available data are from prevalence surveys, but these surveys are generally carried out at arbitrary locations and include nonstandardized and overlapping age groups. To achieve comparability between different surveys, the authors propose the use of transmission models, particularly the Garki model, to convert heterogeneous age prevalence data to a common scale of estimated entomological inoculation rates, vectorial capacity, or force of infection. They apply this approach to the analysis of survey data from Mali, collected in 1965-1998, extracted from the Mapping Malaria Risk in Africa database. They use Bayesian geostatistical models to produce smooth maps of estimates of the entomological inoculation rates obtained from the Garki model, allowing for the effect of environmental covariates. Again using the Garki model, they convert kriged entomological inoculation rates values to age-specific malaria prevalence. The approach makes more efficient use of the available data than do previous malaria mapping methods, and it produces highly plausible maps of malaria distribution.

Adolescent↗
PubMed2006

[Application of Bayesian network in syndrome differentiation system of traditional Chinese medicine].

The concept of syndrome in traditional Chinese medicine (TCM) is a nonlinear, open and complicated huge system. Syndrome differentiation in TCM belongs to cognitive and noetic science. To establish a new syndrome differentiation system based on the key elements of the syndrome is necessary for TCM practitioners to promote differentiation ability and reach consensus on differentiation method. With combination of experience and computation models, the Bayesian network was used in the study of the relationship between the key elements of syndrome and the symptoms, and the relationship among different key elements, in which the computing diagnosis result was identical to the result from an experienced TCM doctor. The study showed that Bayesian network is a good method to deal with the information of symptoms and signs for syndrome differentiation, but it is also not to reflect comprehensively the thinking ability of TCM doctors in doing syndrome differentiation.

Bayes Theorem↗
PubMed2006

A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach.

A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment.

Bayes Theorem↗
PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗
PubMed2004

Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).

Animals↗
PubMed2007

Bayesian analysis of non-homogeneous Markov chains: application to mental health data.

In this paper we present a formal treatment of non-homogeneous Markov chains by introducing a hierarchical Bayesian framework. Our work is motivated by the analysis of correlated categorical data which arise in assessment of psychiatric treatment programs. In our development, we introduce a Markovian structure to describe the non-homogeneity of transition patterns. In doing so, we introduce a logistic regression set-up for Markov chains and incorporate covariates in our model. We present a Bayesian model using Markov chain Monte Carlo methods and develop inference procedures to address issues encountered in the analyses of data from psychiatric treatment programs. Our model and inference procedures are implemented to some real data from a psychiatric treatment study.

Adolescent↗
PubMed2005

Bayesian calibration of process-based forest models: bridging the gap between models and data.

Process-based forest models generally have many parameters, multiple outputs of interest and a small underlying empirical database. These characteristics hamper parameterization. Bayesian calibration offers a solution to the calibration problem because it applies to models of any type or size. It provides parameter estimates, with measures of uncertainty and correlation among the parameters. The procedure begins by quantifying the uncertainty about parameter values in the form of a prior probability distribution. Then data on the output variables are used to update the parameter distribution by means of Bayes' Theorem. This yields a posterior calibrated distribution for the parameters, which can be summarized in the form of a mean vector and variance matrix. The predictive uncertainty of the model can be quantified by running it with different parameter settings, sampled from the posterior distribution. In a further step, one may evaluate the posterior probability of the model itself (rather than that of the parameters) and compare that against the probability of other models, to aid in model selection or improvement. Bayesian calibration of process-based models cannot be performed analytically, so the posterior parameter distribution must be approximated in the form of a representative sample of parameter values. This can be achieved by means of Markov Chain Monte Carlo simulation, which is suitable for process-based models because of its simplicity and because it does not require advance knowledge of the shape of the posterior distribution. Despite the suitability of Bayesian calibration, the technique has rarely been used in forestry research. We introduce the method, using the example of a typical forest model. Further, we show that reductions in parameter uncertainty, and thus in output uncertainty, can be effected by increasing the variety of data, increasing the accuracy of measurements and increasing the length of time series.

Algorithms↗