Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Infant mortality model for lifetime data

In this paper we introduce a parametric model for handling lifetime data where an early lifetime can be related to the infant-mortality failure or to the wear processes but we do not know which risk is responsible for the failure. The maximum likelihood approach and the sampling-based approach are used to get the inferences of interest. Some special cases of the proposed model are studied via Monte Carlo methods for size and power of hypothesis tests. To illustrate the proposed methodology, we introduce an example consisting of a real data set.

Large time behaviour for functional differential equations with dominant eigenvalues of arbitrary order

The spectral theory for linear autonomous neutral functional differential equations (FDE) yields explicit formulas for the large time behaviour of solutions. Our results are based on resolvent computations and Dunford calculus, applied to establish explicit formulas for the large time behaviour of solutions of FDE. We investigate in detail a class of two-dimensional systems of FDE. (C) 2009 Elsevier Inc. All rights reserved.

Measurement error models with a general class of error distribution

In general, the normal distribution is assumed for the surrogate of the true covariates in the classical error model. This paper considers a class of distributions, which includes the normal one, for the variables subject to error. An estimation approach yielding consistent estimators is developed and simulation studies reported.

Dimensões dos arcos dentários na má oclusão Classe II, divisão 1, com deficiência mandibular

OBJETIVO: identificar alterações dimensionais nos arcos dentários superior e inferior na má oclusão Classe II, divisão 1, com deficiência mandibular (Padrão esquelético II). MÉTODOS: 48 pacientes com má oclusão Classe II, igualmente divididos quanto ao gênero, foram comparados com 51 indivíduos com oclusão normal, sendo 22 do gênero masculino e 29 do gênero feminino. Todos os 99 indivíduos estavam no estágio de dentadura permanente, com os segundos molares permanentes irrompidos ou em irrupção, com idade média de 12 anos e 5 meses (desvio-padrão de 1 ano e 3 meses), numa faixa etária oscilando entre 11 anos e 4 meses e 20 anos. CONCLUSÃO: os resultados permitem concluir que, na má oclusão Classe II, divisão 1 com deficiência mandibular, o arco dentário superior encontra-se alterado, mostrando-se atrésico e mais longo, enquanto o arco dentário inferior é pouco influenciado pela discrepância sagital de Classe II.

Canine population dynamics: the potential effect of sterilization campaigns

Objective. To analyze, through mathematical modeling, the potential ability of sterilization campaigns to reduce the population density of pet dogs. Methods. Mathematical models were constructed to simulate the canine population dynamics and project the results of control strategies based on several sterilization rates. Results. Even at high sterilization rates (for example, 0.80 year(-1)), it would take approximately 5 years to reduce density by 20%. Even so, other sources of population growth, such as the importing of dogs from other geographic areas, could outweigh the effects of a sterilization program. Conclusions. A program`s effectiveness is contingent upon not only on the sterilization rate, but also the rate of population growth. Sterilization campaigns may potentially reduce population density, but this reduction may not be immediately evident.

Taxonomic and Phylogenetic Relationships Between Species of the Genus Culex (Diptera: Culicidae) From Brazil Inferred From the Cytochrome c Oxidase I Mitochondrial Gene

Species of the genus Culex Linnaeus have been incriminated as the main vectors of lymphatic filariases and are important vectors of arboviruses, including West Nile virus. Sequences corresponding to a fragment of 478 bp of the cytochrome c oxidase subunit I gene, which includes part of the barcode region, of 37 individuals of 17 species of genus Culex were generated to establish relationships among five subgenera, Culex, Phenacomyia, Melanoconion, Microculex, and Carrollia, and one species of the genus Lutzia that occurs in Brazil. Bayesian methods were employed for the phylogenetic analyses. Results of sequence comparisons showed that individuals identified as Culex dolosus, Culex mollis, and Culex imitator possess high intraspecific divergence (3.1, 2.3, and 3.5%, respectively) when using the Kimura two parameters model. These differences were associated either with distinct morphological characteristics of the male genitalia or larval and pupal stages, suggesting that these may represent species complexes. The Bayesian topology suggested that the genus and subgenus Culex are paraphyletic relative to Lutzia and Phenacomyia, respectively. The cytochrome c oxidase subunit I sequences may be a useful tool to both estimate phylogenetic relationships and identify morphologically similar species of the genus Culex.

Evolutionary history of Ramphastos toucans: Molecular phylogenetics, temporal diversification, and biogeography

The toucan genus Ramphastos (Piciformes: Ramphastidae) has been a model in the formulation of Neotropical paleobiogeographic hypotheses. Weckstein (2005) reported on the phylogenetic history of this genus based on three mitochondrial genes, but some relationships were weakly supported and one of the subspecies of R. vitellinus (citreolaemus) was unsampled. This study expands on Weckstein (2005) by adding more DNA sequence data (including a nuclear marker) and more samples, including R v. citreolaemus. Maximum parsimony, maximum likelihood, and Bayesian methods recovered similar trees, with nodes showing high support. A monophyletic R. vitellinus complex was strongly supported as the sister-group to R. brevis. The results also confirmed that the southeastern and northern populations of R. vitellinus ariel are paraphyletic. X v. citreolaemus is sister to the Amazonian subspecies of the vitellinus complex. Using three protein-coding genes (COI, cytochrome-b and ND2) and interval-calibrated nodes under a Bayesian relaxed-clock framework, we infer that ramphastid genera originated in the middle Miocene to early Pliocene, Ramphastos species originated between late Miocene and early Pleistocene, and intra-specific divergences took place throughout the Pleistocene. Parsimony-based reconstruction of ancestral areas indicated that evolution of the four trans-Andean Ramphastos taxa (R. v. citreolaemus, R. a. swainsonii, R. brevis and R. sulfuratus) was associated with four independent dispersals from the cis-Andean region. The last pulse of Andean uplift may have been important for the evolution of R. sulfuratus, whereas the origin of the other trans-Andean Ramphastos taxa is consistent with vicariance due to drying events in the lowland forests north of the Andes. Estimated rates of molecular evolution were higher than the ""standard"" bird rate of 2% substitutions/site/million years for two of the three genes analyzed (cytochrome-b and ND2). (C) 2009 Elsevier Inc. All rights reserved.

On the influence of imputation in classification: practical issues

The substitution of missing values, also called imputation, is an important data preparation task for many domains. Ideally, the substitution of missing values should not insert biases into the dataset. This aspect has been usually assessed by some measures of the prediction capability of imputation methods. Such measures assume the simulation of missing entries for some attributes whose values are actually known. These artificially missing values are imputed and then compared with the original values. Although this evaluation is useful, it does not allow the influence of imputed values in the ultimate modelling task (e.g. in classification) to be inferred. We argue that imputation cannot be properly evaluated apart from the modelling task. Thus, alternative approaches are needed. This article elaborates on the influence of imputed values in classification. In particular, a practical procedure for estimating the inserted bias is described. As an additional contribution, we have used such a procedure to empirically illustrate the performance of three imputation methods (majority, naive Bayes and Bayesian networks) in three datasets. Three classifiers (decision tree, naive Bayes and nearest neighbours) have been used as modelling tools in our experiments. The achieved results illustrate a variety of situations that can take place in the data preparation practice.

Positive solutions of the p-Laplacian involving a superlinear nonlinearity with zeros

Using a combination of several methods, such as variational methods. the sub and supersolutions method, comparison principles and a priori estimates. we study existence, multiplicity, and the behavior with respect to lambda of positive solutions of p-Laplace equations of the form -Delta(p)u = lambda h(x, u), where the nonlinear term has p-superlinear growth at infinity, is nonnegative, and satisfies h(x, a(x)) = 0 for a suitable positive function a. In order to manage the asymptotic behavior of the solutions we extend a result due to Redheffer and we establish a new Liouville-type theorem for the p-Laplacian operator, where the nonlinearity involved is superlinear, nonnegative, and has positive zeros. (C) 2009 Elsevier Inc. All rights reserved.

On the dominance of roots of characteristic equations for neutral functional differential equations

We present a sufficient condition for a zero of a function that arises typically as the characteristic equation of a linear functional differential equations of neutral type, to be simple and dominant. This knowledge is useful in order to derive the asymptotic behaviour of solutions of such equations. A simple characteristic equation, arisen from the study of delay equations with small delay, is analyzed in greater detail. (C) 2009 Elsevier Inc. All rights reserved.

Evaluation of hexose and pentose in pre-cultivation of Candida guilliermondii on the key enzymes for xylitol production in sugarcane hemicellulosic hydrolysate

The evaluation of hexose and pentose in pre-cultivation of Candida guilliermondii FTI 20037 yeast on xylose reductase (XR) and xylitol dehydrogenase (XDH) enzymes activities was performed during fermentation in sugarcane bagasse hemicellulosic hydrolysate. The xylitol production was evaluated by using cells previously growth in 30.0 gl(-1) xylose, 30.0 gl(-1) glucose and in both sugars mixture (30.0 gl(-1) xylose and 2.0 gl(-1) glucose). The vacuum evaporated hydrolysate (80 gl(-1)) was detoxificated by ion exchange resin (A-860S; A500PS and C-150-Purolite(A (R))). The total phenolic compounds and acetic acid were 93.0 and 64.9%, respectively, removed by the resin hydrolysate treatment. All experiments were carried out in Erlenmeyer flasks at 200 rpm, 30A degrees C. The maximum XR (0.618 Umg (Prot) (-1) ) and XDH (0.783 Umg (Prot) (-1) ) enzymes activities was obtained using inoculum previously growth in both sugars mixture. The highest cell concentration (10.6 gl(-1)) was obtained with inoculum pre-cultivated in the glucose. However, the xylitol yield and xylitol volumetric productivity were favored using the xylose as carbon source. In this case, it was observed maximum xylose (81%) and acetic acid (100%) consumption. It is very important to point out that maximum enzymatic activities were obtained when the mixture of sugars was used as carbon source of inoculum, while the highest fermentative parameters were obtained when xylose was used.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.