A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.

Arbitrage-Free Conditions and Hedging Strategies for Markets with Penalty Costs on Short Positions

We consider a discrete-time financial model in a general sample space with penalty costs on short positions. We consider a friction market closely related to the standard one except that withdrawals from the portfolio value proportional to short positions are made. We provide necessary and sufficient conditions for the nonexistence of arbitrages in this situation and for a self-financing strategy to replicate a contingent claim. For the finite-sample space case, this result leads to an explicit and constructive procedure for obtaining perfect hedging strategies.

Viral contamination during sequential phacoemulsification surgeries in an experimental model

Purpose: To determine the incidence of Piry virus contamination among surgical instruments used with disposable accessories for phacoemulsification during sequential surgeries. Methods: An experimental model was created with 4 pigs' eyes that were contaminated with Piry virus and 4 pigs' eyes that were not contaminated. Phacoemulsification was performed on the eyes, alternating between the contaminated and non-contaminated eyes. From one surgery to another, the operating fields, gloves, scalpel, tweezers, needles, syringes, tips and bag collector from the phacoemulsification machine were exchanged; only the hand piece and the irrigation and aspiration systems were maintained. Results: In the collector bag, three samples from the contaminated eyes (3/4) were positive, and two samples from the non-contaminated (2/4) eyes were also positive; at the tip, one sample from the contaminated eyes (1/4) and two samples of the noncontaminated eyes (2/4) yielded positive results. In the irrigation system, one sample from a non-contaminated eye (1/4) was positive, and in the aspiration system, two samples from contaminated eyes (2/4) and two samples from non-contaminated eyes (2/4) were positive. In the gloves, the samples were positive in two samples from the non-contaminated eyes (2/4) and in two samples from the contaminated eyes (2/4). In the scalpel samples, three contaminated eyes (3/4) and none of the non-contaminated eyes (0/4) were positive; finally, two samples from the anterior chambers of the non-contaminated eyes gathered after surgery were positive. Conclusions: In two non-contaminated eyes, the presence of genetic material was detected after phacoemulsification surgery, demonstrating that the transmission of the genetic material of the Piry virus occurred at some point during the surgery on these non-contaminated eyes when the hand piece and irrigation and aspiration systems were reused between surgeries.

A model of road effect using line integrals and a test of the performance of two new road indices using the distribution of small mammals in an Atlantic Forest landscape

Effects of roads on wildlife and its habitat have been measured using metrics, such as the nearest road distance, road density, and effective mesh size. In this work we introduce two new indices: (1) Integral Road Effect (IRE), which measured the sum effects of points in a road at a fixed point in the forest; and (2) Average Value of the Infinitesimal Road Effect (AVIRE), which measured the average of the effects of roads at this point. IRE is formally defined as the line integral of a special function (the infinitesimal road effect) along the curves that model the roads, whereas AVIRE is the quotient of IRE by the length of the roads. Combining tools of ArcGIS software with a numerical algorithm, we calculated these and other road and habitat cover indices in a sample of points in a human-modified landscape in the Brazilian Atlantic Forest, where data on the abundance of two groups of small mammals (forest specialists and habitat generalists) were collected in the field. We then compared through the Akaike Information Criterion (AIC) a set of candidate regression models to explain the variation in small mammal abundance, including models with our two new road indices (AVIRE and IRE) or models with other road effect indices (nearest road distance, mesh size, and road density), and reference models (containing only habitat indices, or only the intercept without the effect of any variable). Compared to other road effect indices, AVIRE showed the best performance to explain abundance of forest specialist species, whereas the nearest road distance obtained the best performance to generalist species. AVIRE and habitat together were included in the best model for both small mammal groups, that is, higher abundance of specialist and generalist small mammals occurred where there is lower average road effect (less AVIRE) and more habitat. Moreover, AVIRE was not significantly correlated with habitat cover of specialists and generalists differing from the other road effect indices, except mesh size, which allows for separating the effect of roads from the effect of habitat on small mammal communities. We suggest that the proposed indices and GIS procedures could also be useful to describe other spatial ecological phenomena, such as edge effect in habitat fragments. (C) 2012 Elsevier B.V. All rights reserved.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

Background: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Metallicities for six nearby open clusters from high-resolution spectra of giant stars [Fe/H] values for a planet search sample

We present a study of the stellar parameters and iron abundances of 18 giant stars in six open clusters. The analysis was based on high-resolution and high-S/N spectra obtained with the UVES spectrograph (VLT-UT2). The results complement our previous study where 13 clusters were already analyzed. The total sample of 18 clusters is part of a program to search for planets around giant stars. The results show that the 18 clusters cover a metallicity range between -0.23 and +0.23 dex. Together with the derivation of the stellar masses, these metallicities will allow the metallicity and mass effects to be disentangled when analyzing the frequency of planets as a function of these stellar parameters.

MODULARITY, NOISE, AND NATURAL SELECTION

Most biological systems are formed by component parts that are to some degree interrelated. Groups of parts that are more associated among themselves and are relatively autonomous from others are called modules. One of the consequences of modularity is that biological systems usually present an unequal distribution of the genetic variation among traits. Estimating the covariance matrix that describes these systems is a difficult problem due to a number of factors such as poor sample sizes and measurement errors. We show that this problem will be exacerbated whenever matrix inversion is required, as in directional selection reconstruction analysis. We explore the consequences of varying degrees of modularity and signal-to-noise ratio on selection reconstruction. We then present and test the efficiency of available methods for controlling noise in matrix estimates. In our simulations, controlling matrices for noise vastly improves the reconstruction of selection gradients. We also perform an analysis of selection gradients reconstruction over a New World Monkeys skull database to illustrate the impact of noise on such analyses. Noise-controlled estimates render far more plausible interpretations that are in full agreement with previous results.

Newtonian acceleration scales in spiral galaxies

We revisit the issue of the constancy of the dark matter (DM) and baryonic Newtonian acceleration scales within the DM scale radius by considering a large sample of late-type galaxies. We rely on a Markov Chain Monte Carlo method to estimate the parameters of the halo model and the stellar mass-to-light ratio and then propagate the uncertainties from the rotation curve data to the estimate of the acceleration scales. This procedure allows us to compile a catalogue of 58 objects with estimated values of the B-band absolute magnitude M-B, the virial mass M-vir, and the DM and baryonic Newtonian accelerations (denoted as g(DM)(r(0)) and g(bar)(r(0)), respectively) within the scale radius r(0) which we use to investigate whether it is possible to define a universal acceleration scale. We find a weak but statistically meaningful correlation with M-vir thus making us argue against the universality of the acceleration scales. However, the results somewhat depend on the sample adopted so that a careful analysis of selection effects should be carried out before any definitive conclusion can be drawn.

Molecular mapping of the regenerative niche in a murine model of myocardial infarction

Adult stem cells are distributed through the whole organism, and present a great potential for the therapy of different types of disease. For the design of efficient therapeutic strategies, it is important to have a more detailed understanding of their basic biological characteristics, as well as of the signals produced by damaged tissues and to which they respond. Myocardial infarction (MI), a disease caused by a lack of blood flow supply in the heart, represents the most common cause of morbidity and mortality in the Western world. Stem cell therapy arises as a promising alternative to conventional treatments, which are often ineffective in preventing loss of cardiomyocytes and fibrosis. Cell therapy protocols must take into account the molecular events that occur in the regenerative niche of MI. In the present study, we investigated the expression profile of ten genes coding for chemokines or cytokines in a murine model of MI, aiming at the characterization of the regenerative niche. MI was induced in adult C57BL/6 mice and heart samples were collected after 24 h and 30 days, as well as from control animals, for quantitative RT-PCR. Expression of the chemokine genes CCL2, CCL3, CCL4, CCL7, CXCL2 and CXCL10 was significantly increased 24 h after infarction, returning to baseline levels on day 30. Expression of the CCL8 gene significantly increased only on day 30, whereas gene expression of CXCL12 and CX3CL1 were not significantly increased in either ischemic period. Finally, expression of the IL-6 gene increased 24 h after infarction and was maintained at a significantly higher level than control samples 30 days later. These results contribute to the better knowledge of the regenerative niche in MI, allowing a more efficient selection or genetic manipulation of cells in therapeutic protocols.

Three-feature model to reproduce the topology of citation networks and the effects from authors' visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely "graphenes" and "complex networks", thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two - in-degree (i.e. citation counts) and age of publication - had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. (C) 2012 Elsevier Ltd. All rights reserved.

Effect of external fields in Axelrod's model of social dynamics

The study of the effects of spatially uniform fields on the steady-state properties of Axelrod's model has yielded plenty of counterintuitive results. Here, we reexamine the impact of this type of field for a selection of parameters such that the field-free steady state of the model is heterogeneous or multicultural. Analyses of both one- and two-dimensional versions of Axelrod's model indicate that the steady state remains heterogeneous regardless of the value of the field strength. Turning on the field leads to a discontinuous decrease on the number of cultural domains, which we argue is due to the instability of zero-field heterogeneous absorbing configurations. We find, however, that spatially nonuniform fields that implement a consensus rule among the neighborhood of the agents enforce homogenization. Although the overall effects of the fields are essentially the same irrespective of the dimensionality of the model, we argue that the dimensionality has a significant impact on the stability of the field-free homogeneous steady state.

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Coping Style and Depressive Symptomatology during Pregnancy in a Private Setting Sample

Aim: to evaluate the association of antenatal depressive symptomatology (AD) with life events and coping styles, the hypothesis was that certain coping strategies are associated to depressive symptomatology. Methods: we performed a cross sectional study of 312 women attending a private clinic in the city of Osasco, Sao Paulo from 27/05/1998 to 13/05/2002. The following instruments were used: Beck Depression Inventory (BDI), Holmes and Rahe Schedule of Recent Events (SSRS), Folkman and Lazarus Ways of Coping Questionnaire and questionnaire with social-demographic and obstetric data. Inclusion criteria: women with 110 past history of depression, psychiatric treatment, alcohol or drug abuse and no clinical-obstetrical complications. Odds ratios and 95% CI were used to examine the association between AD (according to BDI) and exposures variables. Hypothesis testing was done with chi(2) tests and a p value < .05. Results: AD occurred in 21.1% of pregnant women. By the univariate analyses, education, number of pregnancies, previous abortion, husband income, situation of marriage and score of SSRS were associated with AD. All coping styles were associated with AD, except seeking support and positive reappraisal. By the multivariate analyses, four coping styles were kept in the final model: confront (p = .039), accepting responsibility (p < .001), escape-avoidance (p = .002), problem-solving (p = .005). Conclusions: AD was highly prevalent and was associated with maladaptive coping styles.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

A feature selection approach for identification of signature genes from SAGE data

Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.