985 resultados para logistic models


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BACKGROUND: Heart failure (HF) prevention strategies require biomarkers that identify disease manifestation. Increases in B-type natriuretic peptide (BNP) correlate with increased risk of cardiovascular events and HF development. We hypothesize that coronary sinus serum from a high BNP hypertensive population reflects an active pathological process and can be used for biomarker exploration. Our aim was to discover differentially expressed disease-associated proteins that identify patients with ventricular dysfunction and HF.

METHODS AND RESULTS: Coronary sinus serum from 11 asymptomatic, hypertensive patients underwent quantitative differential protein expression analysis by 2-dimensional difference gel electrophoresis. Proteins were identified using mass spectrometry and then studied by enzyme-linked immunosorbent assay in sera from 40 asymptomatic, hypertensive patients and 105 patients across the spectrum of ventricular dysfunction (32 asymptomatic left ventricular diastolic dysfunction, 26 diastolic HF, and 47 systolic HF patients). Leucine-rich α2-glycoprotein (LRG) was consistently overexpressed in high BNP serum. LRG levels correlate significantly with BNP in hypertensive, asymptomatic left ventricular diastolic dysfunction, diastolic HF, and systolic HF patient groups (P≤0.05). LRG levels were able to identify HF independent of BNP. LRG correlates with coronary sinus serum levels of tumor necrosis factor-α (P=0.009) and interleukin-6 (P=0.021). LRG is expressed in myocardial tissue and correlates with transforming growth factor-βR1 (P<0.001) and α-smooth muscle actin (P=0.025) expression.

CONCLUSIONS: LRG was identified as a serum biomarker that accurately identifies patients with HF. Multivariable modeling confirmed that LRG is a stronger identifier of HF than BNP and this is independent of age, sex, creatinine, ischemia, β-blocker therapy, and BNP.

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BACKGROUND: EGFR overexpression occurs in 27-55% of oesophagogastric adenocarcinomas, and correlates with poor prognosis. We aimed to assess addition of the anti-EGFR antibody panitumumab to epirubicin, oxaliplatin, and capecitabine (EOC) in patients with advanced oesophagogastric adenocarcinoma. METHODS: In this randomised, open-label phase 3 trial (REAL3), we enrolled patients with untreated, metastatic, or locally advanced oesophagogastric adenocarcinoma at 63 centres (tertiary referral centres, teaching hospitals, and district general hospitals) in the UK. Eligible patients were randomly allocated (1:1) to receive up to eight 21-day cycles of open-label EOC (epirubicin 50 mg/m(2) and oxaliplatin 130 mg/m(2) on day 1 and capecitabine 1250 mg/m(2) per day on days 1-21) or modified-dose EOC plus panitumumab (mEOC+P; epirubicin 50 mg/m(2) and oxaliplatin 100 mg/m(2) on day 1, capecitabine 1000 mg/m(2) per day on days 1-21, and panitumumab 9 mg/kg on day 1). Randomisation was blocked and stratified for centre region, extent of disease, and performance status. The primary endpoint was overall survival in the intention-to-treat population. We assessed safety in all patients who received at least one dose of study drug. After a preplanned independent data monitoring committee review in October, 2011, trial recruitment was halted and panitumumab withdrawn. Data for patients on treatment were censored at this timepoint. This study is registered with ClinicalTrials.gov, number NCT00824785. FINDINGS: Between June 2, 2008, and Oct 17, 2011, we enrolled 553 eligible patients. Median overall survival in 275 patients allocated EOC was 11.3 months (95% CI 9.6-13.0) compared with 8.8 months (7.7-9.8) in 278 patients allocated mEOC+P (hazard ratio [HR] 1.37, 95% CI 1.07-1.76; p=0.013). mEOC+P was associated with increased incidence of grade 3-4 diarrhoea (48 [17%] of 276 patients allocated mEOC+P vs 29 [11%] of 266 patients allocated EOC), rash (29 [11%] vs two [1%]), mucositis (14 [5%] vs none), and hypomagnesaemia (13 [5%] vs none) but reduced incidence of haematological toxicity (grade ≥ 3 neutropenia 35 [13%] vs 74 [28%]). INTERPRETATION: Addition of panitumumab to EOC chemotherapy does not increase overall survival and cannot be recommended for use in an unselected population with advanced oesophagogastric adenocarcinoma. FUNDING: Amgen, UK National Institute for Health Research Biomedical Research Centre.

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PURPOSE: To evaluate the addition of cetuximab to neoadjuvant chemotherapy before chemoradiotherapy in high-risk rectal cancer. PATIENTS AND METHODS: Patients with operable magnetic resonance imaging-defined high-risk rectal cancer received four cycles of capecitabine/oxaliplatin (CAPOX) followed by capecitabine chemoradiotherapy, surgery, and adjuvant CAPOX (four cycles) or the same regimen plus weekly cetuximab (CAPOX+C). The primary end point was complete response (CR; pathologic CR or, in patients not undergoing surgery, radiologic CR) in patients with KRAS/BRAF wild-type tumors. Secondary end points were radiologic response (RR), progression-free survival (PFS), overall survival (OS), and safety in the wild-type and overall populations and a molecular biomarker analysis. RESULTS: One hundred sixty-five eligible patients were randomly assigned. Ninety (60%) of 149 assessable tumors were KRAS or BRAF wild type (CAPOX, n = 44; CAPOX+C, n = 46), and in these patients, the addition of cetuximab did not improve the primary end point of CR (9% v 11%, respectively; P = 1.0; odds ratio, 1.22) or PFS (hazard ratio [HR], 0.65; P = .363). Cetuximab significantly improved RR (CAPOX v CAPOX+C: after chemotherapy, 51% v 71%, respectively; P = .038; after chemoradiation, 75% v 93%, respectively; P = .028) and OS (HR, 0.27; P = .034). Skin toxicity and diarrhea were more frequent in the CAPOX+C arm. CONCLUSION: Cetuximab led to a significant increase in RR and OS in patients with KRAS/BRAF wild-type rectal cancer, but the primary end point of improved CR was not met.

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Any movement towards sustainable tourism is dependent not only upon the industry and other key stakeholders but also the demand side, namely the tourists. Yet, there is a limited literature from the demand point of view. In this area, contributions to an understanding of tourists’ support to sustainable development are necessary. This paper analyzes the main determinants in tourist behavior regarding the environmental considerations when they are making decisions about their holiday plans. General literature on this issue highlights the need to consider socio-economic variables of the individual as well as the attributes related of their style of living. If the econometric model takes into account all these variables simultaneously, then the linkage between contextual changes and tourists´ behaviour is enriched and it may be estimated more accurately. In this sense, a multilevel approach using a random-intercept logistic models is proposed, since tourists belong to a country are affected by the same contextual variables. The analysis comprises a joint dataset composed by microdata belong to the survey Attitudes of Europeans Towards Tourism, which corresponds to Flash Eurobarometer 281, macrodata from Eurostat (GDP in pps and GDP growth) and additional variables profiles from the 2005 Environmental Sustainability Index. Country-specific effects are calculated across the EU-27 countries, which corroborated that attitudes to the sustainable tourism are heterogeneous geo-graphically. The higher the level of GDP, the lower the level of tourists´ support. These results could be explained because tourists of richer countries already have to pay more tax for envi-ronmental protection. Age, gender and educational attainment are relevant. Motivations for travelling, size of the community, type of the destination, and environmental sustainability indi-cators of the place of residence are also important factors.

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Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

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A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

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The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^

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Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs. The construction of optimal designs for dose-ranging trials with multiple periods is considered in this paper, where the outcome of the trial (the effect of the drug) is considered to be a binary response: the success or failure of a drug to bring about a particular change in the subject after a given amount of time. The carryover effect of each dose from one period to the next is assumed to be proportional to the direct effect. It is shown for a logistic regression model that the efficiency of optimal parallel (single-period) or crossover (two-period) design is substantially greater than a balanced design. The optimal designs are also shown to be robust to misspecification of the value of the parameters. Finally, the parallel and crossover designs are combined to provide the experimenter with greater flexibility.

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In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

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The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

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In this work a new probabilistic and dynamical approach to an extension of the Gompertz law is proposed. A generalized family of probability density functions, designated by Beta* (p, q), which is proportional to the right hand side of the Tsoularis-Wallace model, is studied. In particular, for p = 2, the investigation is extended to the extreme value models of Weibull and Frechet type. These models, described by differential equations, are proportional to the hyper-Gompertz growth model. It is proved that the Beta* (2, q) densities are a power of betas mixture, and that its dynamics are determined by a non-linear coupling of probabilities. The dynamical analysis is performed using techniques of symbolic dynamics and the system complexity is measured using topological entropy. Generally, the natural history of a malignant tumour is reflected through bifurcation diagrams, in which are identified regions of regression, stability, bifurcation, chaos and terminus.

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Many data have been useful to describe the growth of marine mammals, invertebrates and reptiles, seabirds, sea turtles and fishes, using the logistic, the Gom-pertz and von Bertalanffy's growth models. A generalized family of von Bertalanffy's maps, which is proportional to the right hand side of von Bertalanffy's growth equation, is studied and its dynamical approach is proposed. The system complexity is measured using Lyapunov exponents, which depend on two biological parameters: von Bertalanffy's growth rate constant and the asymptotic weight. Applications of synchronization in real world is of current interest. The behavior of birds ocks, schools of fish and other animals is an important phenomenon characterized by synchronized motion of individuals. In this work, we consider networks having in each node a von Bertalanffy's model and we study the synchronization interval of these networks, as a function of those two biological parameters. Numerical simulation are also presented to support our approaches.