13 resultados para SURVIVAL ANALYSIS

em Universidade Federal do Rio Grande do Norte(UFRN)


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Malaria is a disease of global distribution, recognized by governments around the world as a serious public health problem, affecting more than 109 countries and territories and endangering more than 3.3 billion people. The economic costs of this disease are also relevant: the African continent itself has malaria-related costs of about $ 12 billion annually. Nowadays, in addition to chloroquine, Plasmodium falciparum is resistant to many drugs used in the treatment of malaria, such as amodiaquine, mefloquine, quinine and sulfadoxine-pyrimethamine; resistance of Plasmodium vivax to treatments, although less studied, is also reported. Nature, in general, is responsible for the production of most known organic substances, and the plant kingdom is responsible for the most of the chemical diversity known and reported in the literature. Most medicinal plants commercialized in Brazil, however, are of exotic origin, which makes the search for endemic medicinal plants, besides a patent necessity, a fascinating subject of academic research and development. This study aimed to: (i) verify the antimalarial activity of ethanolic and hydroalcoholic extracts of Boerhavia paniculata Rich. And acetonic extract of Clethra scabra Pers. in Swiss albino mice infected by Plasmodium berghei NK65, (ii) observe possible combined effects between the course of infection by P. berghei NK65 and administration of these extracts in Swiss albino mice, and (iii) conduct a preliminary study of the acute toxicity of these extracts in Swiss albino mice. All extracts notable pharmacological activities - with parasite infections inhibitions ranging from 22% to 54%.These characteristics suggest that the activities are relevant, although comparatively lower than the activity displayed by the positive control group (always above 90%). The general framework of survival analysis demonstrates an overall reduction in survival times for all groups. Necroscopy has not pointed no change in color, shape, size and/or consistency in the evaluated organs - the only exception was the livers of rats submitted to treatment to hydroalcoholic extracts: these organs have been presented in a slightly congestive aspect with mass increasing roughly 28% higher than the other two groups and a p-value of 0.0365. The 250 mg/Kg ethanolic group has been pointed out by the Dunn s post test, as the only class with simultaneous inequalities (p<0.05) between positive and negative control groups. The extracts, notably ethanol extract, have, in fact, a vestigial antimalarial activity, although well below from the ones perceived to chloroquine-treated groups; nevertheless, the survival times of the animals fed with the extracts do not rise by presence of such therapy. Both the toxicopharmacological studies of the synergism between the clinical course of malaria and administration of extracts and the isolated evaluation of toxicity allow us to affirm the absence of toxicity of the extracts at the level of CNS and ANS, as well as their non-influence on food and water consumption patterns, until dosages of 500 mg/Kg. Necroscopic analysis leads us to deduct a possible hepatotoxic effect of hydroalcoholic extract at dosages of 500 mg/Kg, and an innocuous tissue activity of the ethanol extract, in the same dosage. We propose a continuation of the studies of these extracts, with protocol modifications capable of addressing more clearly and objectively their pharmacological and toxicological aspects

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The aging process if characterizes for a complex events network, from multidimensional nature, that encloses biological, social, psychic and functional aspects. The alteration of one or more aspects can speed up the aging process, anticipating limitations and until the death in the aged. For an adjusted confrontation of this question is necessary an interdisciplinary vision, in which the some areas of the knowledge can interact and with this to intervenes of the best possible form. Then, information derived from studies of aspects related to incidence, morbidity-mortality and transition patterns, involved in the health-illness process can more accurately identify risk groups thereby establishing links between social factors, illness, incapacity and death. Thus, this study aimed to identify, by a multidimensional vision, the risk factors of mortality in a coorth of elderly in a city in the interior of the state of Rio Grande do Norte (RN), Brazil. A prospective study carried out in Santa Cruz RN, where 310 elderly were randomly selected to form a baseline. The follow-up was 53 months. The predictive variables were divided into sociodemographic, physical health, neuropsychiatric and functional capacity. The statistical analysis carried out by bivariate analysis, survival analysis, followed by binary logistic regression and Cox regression, in the multivariate analysis, considering significant levels p < 0.05 and confidence interval (CI) of 95%. A total of 60 (19.3%) elderly died during the follow-up, where cardiovascular disease was the main cause. The survival was approximately 24.8 months. The study of general survival showed, at 12, 24, 36, and 48 months of observation, a survival rate of 97%, 54%, 31%, and 5% respectively, with a statistical difference in survival only observed for the variables of cognitive function and Basic Activities of Daily Living. In the logistic regression analysis, the risk factors identified were cognitive deficits (OR = 8.74), poor perception of health (OR = 3.89) and dependence for Basic Activities of Daily Living (OR = 3.96). In the Cox analysis, as well as dependence for Basic Activities of Daily Living (HR = 3.17), cognitive deficit (HR = 4.30) and stroke (CVA) (HR = 3.49) continued as independent risk factors for death. The risk factors found in the study can be interpreted as the primary predictors for death among elderly members of the community. Therefore, improvements in health conditions, with actions towards sustaining an autonomous life with special attention for elderly with cognitive impairment, could mean additional healthy quality of life, resulting in the reduction of premature mortality in this population

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The problems of combinatory optimization have involved a large number of researchers in search of approximative solutions for them, since it is generally accepted that they are unsolvable in polynomial time. Initially, these solutions were focused on heuristics. Currently, metaheuristics are used more for this task, especially those based on evolutionary algorithms. The two main contributions of this work are: the creation of what is called an -Operon- heuristic, for the construction of the information chains necessary for the implementation of transgenetic (evolutionary) algorithms, mainly using statistical methodology - the Cluster Analysis and the Principal Component Analysis; and the utilization of statistical analyses that are adequate for the evaluation of the performance of the algorithms that are developed to solve these problems. The aim of the Operon is to construct good quality dynamic information chains to promote an -intelligent- search in the space of solutions. The Traveling Salesman Problem (TSP) is intended for applications based on a transgenetic algorithmic known as ProtoG. A strategy is also proposed for the renovation of part of the chromosome population indicated by adopting a minimum limit in the coefficient of variation of the adequation function of the individuals, with calculations based on the population. Statistical methodology is used for the evaluation of the performance of four algorithms, as follows: the proposed ProtoG, two memetic algorithms and a Simulated Annealing algorithm. Three performance analyses of these algorithms are proposed. The first is accomplished through the Logistic Regression, based on the probability of finding an optimal solution for a TSP instance by the algorithm being tested. The second is accomplished through Survival Analysis, based on a probability of the time observed for its execution until an optimal solution is achieved. The third is accomplished by means of a non-parametric Analysis of Variance, considering the Percent Error of the Solution (PES) obtained by the percentage in which the solution found exceeds the best solution available in the literature. Six experiments have been conducted applied to sixty-one instances of Euclidean TSP with sizes of up to 1,655 cities. The first two experiments deal with the adjustments of four parameters used in the ProtoG algorithm in an attempt to improve its performance. The last four have been undertaken to evaluate the performance of the ProtoG in comparison to the three algorithms adopted. For these sixty-one instances, it has been concluded on the grounds of statistical tests that there is evidence that the ProtoG performs better than these three algorithms in fifty instances. In addition, for the thirty-six instances considered in the last three trials in which the performance of the algorithms was evaluated through PES, it was observed that the PES average obtained with the ProtoG was less than 1% in almost half of these instances, having reached the greatest average for one instance of 1,173 cities, with an PES average equal to 3.52%. Therefore, the ProtoG can be considered a competitive algorithm for solving the TSP, since it is not rare in the literature find PESs averages greater than 10% to be reported for instances of this size.

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In this work, we study the survival cure rate model proposed by Yakovlev et al. (1993), based on a competing risks structure concurring to cause the event of interest, and the approach proposed by Chen et al. (1999), where covariates are introduced to model the risk amount. We focus the measurement error covariates topics, considering the use of corrected score method in order to obtain consistent estimators. A simulation study is done to evaluate the behavior of the estimators obtained by this method for finite samples. The simulation aims to identify not only the impact on the regression coefficients of the covariates measured with error (Mizoi et al. 2007) but also on the coefficients of covariates measured without error. We also verify the adequacy of the piecewise exponential distribution to the cure rate model with measurement error. At the end, model applications involving real data are made

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In this work we study the survival cure rate model proposed by Yakovlev (1993) that are considered in a competing risk setting. Covariates are introduced for modeling the cure rate and we allow some covariates to have missing values. We consider only the cases by which the missing covariates are categorical and implement the EM algorithm via the method of weights for maximum likelihood estimation. We present a Monte Carlo simulation experiment to compare the properties of the estimators based on this method with those estimators under the complete case scenario. We also evaluate, in this experiment, the impact in the parameter estimates when we increase the proportion of immune and censored individuals among the not immune one. We demonstrate the proposed methodology with a real data set involving the time until the graduation for the undergraduate course of Statistics of the Universidade Federal do Rio Grande do Norte

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In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates

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The main specie of marine shrimp raised at Brazil and in the world is Litopenaeus vannamei, which had arrived in Brazil in the `80s. However, the entry of infectious myonecrosis virus (IMNV), causing the infectious myonecrosis disease in marine shrimps, brought economic losses to the national shrimp farming, with up to 70% of mortality in the shrimp production. In this way, the objective was to evaluate the survival of shrimps Litopenaeus vannamei infected with IMNV using the non parametric estimator of Kaplan-Meier and a model of frailty for grouped data. It were conducted three tests of viral challenges lasting 20 days each, at different periods of the year, keeping the parameters of pH, temperature, oxygen and ammonia monitored daily. It was evaluated 60 full-sib families of L. vannamei infected by IMNV in each viral challenge. The confirmation of the infection by IMNV was performed using the technique of PCR in real time through Sybr Green dye. Using the Kaplan-Meier estimator it was possible to detect significant differences (p <0.0001) between the survival curves of families and tanks and also in the joint analysis between viral challenges. It were estimated in each challenge, genetic parameters such as genetic value of family, it`s respective rate risk (frailty), and heritability in the logarithmic scale through the frailty model for grouped data. The heritability estimates were respectively 0.59; 0.36; and 0.59 in the viral challenges 1; 2; and 3, and it was also possible to identify families that have lower and higher rates of risk for the disease. These results can be used for selecting families more resistant to the IMNV infection and to include characteristic of disease resistance in L. vannamei into the genetic improvement programs

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Among the traits of economic importance to dairy cattle livestock those related to sexual precocity and longevity of the herd are essential to the success of the activity, because the stayability time of a cow in a herd is determined by their productive and reproductive lives. In Brazil, there are few studies about the reproductive efficiency of Swiss-Brown cows and no study was found using the methodology of survival analysis applied to this breed. Thus, in the first chapter of this study, the age at first calving from Swiss-Brown heifers was analyzed as the time until the event by the nonparametric method of Kaplan-Meier and the gamma shared frailty model, under the survival analysis methodology. Survival and hazard rate curves associated with this event were estimated and identified the influence of covariates on such time. The mean and median times at the first calving were 987.77 and 1,003 days, respectively, and significant covariates by the Log-Rank test, through Kaplan-Meier analysis, were birth season, calving year, sire (cow s father) and calving season. In the analysis by frailty model, the breeding values and the frailties of the sires (fathers) for the calving were predicted modeling the risk function of each cow as a function of the birth season as fixed covariate and sire as random covariate. The frailty followed the gamma distribution. Sires with high and positive breeding values possess high frailties, what means shorter survival time of their daughters to the event, i.e., reduction in the age at first calving of them. The second chapter aimed to evaluate the longevity of dairy cows using the nonparametric Kaplan-Meier and the Cox and Weibull proportional hazards models. It were simulated 10,000 records of the longevity trait from Brown-Swiss cows involving their respective times until the occurrence of five consecutive calvings (event), considered here as typical of a long-lived cow. The covariates considered in the database were age at first calving, herd and sire (cow s father). All covariates had influence on the longevity of cows by Log-Rank and Wilcoxon tests. The mean and median times to the occurrence of the event were 2,436.285 and 2,437 days, respectively. Sires that have higher breeding values also have a greater risk of that their daughters reach the five consecutive calvings until 84 months

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The evaluation criteria of the cases treated with dental implants are based on clinical and radiographic tests. In this context it is important to conduct research to determine prognosis of different types of prosthetic rehabilitation and determination of the main problems affecting this type of treatment. Thus, the objective of this study was to assess the prosthetic conditions of individuals rehabilitated with implant-supported prosthesis. In this cross-sectional study 153 patients were treated, accounting for a sample of 509 implants. The failures were observed by clinical and radiographic examination. The results showed that the fracture (0.2%) loss (0.4%) and loosening of the screws (3.3%) were failures are less frequent. The fracture structures as the resin (12.4%), porcelain (5.5%) and metallic (1.5%), loss of resin that covers the screw (23.8%) and loss of retention overdentures on implants (18.6%) had a higher occurrence. The failure of adaptation between the abutment and the implant (6.9%) and especially between the prosthesis and the abutment (25.4%) had a high prevalence and, when related to other parameters showed a significant association, particularly with the cemented prosthesis (OR = 6.79). It can be concluded that to minimize the appearance of failures, protocols must be observed from diagnosis to the settlement and control of prostheses on implants, particularly with respect to technical steps of the making of the prosthesis and care in radiographic evaluating the fit between their components

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Due to great difficulty of accurate solution of Combinatorial Optimization Problems, some heuristic methods have been developed and during many years, the analysis of performance of these approaches was not carried through in a systematic way. The proposal of this work is to make a statistical analysis of heuristic approaches to the Traveling Salesman Problem (TSP). The focus of the analysis is to evaluate the performance of each approach in relation to the necessary computational time until the attainment of the optimal solution for one determined instance of the TSP. Survival Analysis, assisted by methods for the hypothesis test of the equality between survival functions was used. The evaluated approaches were divided in three classes: Lin-Kernighan Algorithms, Evolutionary Algorithms and Particle Swarm Optimization. Beyond those approaches, it was enclosed in the analysis, a memetic algorithm (for symmetric and asymmetric TSP instances) that utilizes the Lin-Kernighan heuristics as its local search procedure

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In survival analysis, the response is usually the time until the occurrence of an event of interest, called failure time. The main characteristic of survival data is the presence of censoring which is a partial observation of response. Associated with this information, some models occupy an important position by properly fit several practical situations, among which we can mention the Weibull model. Marshall-Olkin extended form distributions other a basic generalization that enables greater exibility in adjusting lifetime data. This paper presents a simulation study that compares the gradient test and the likelihood ratio test using the Marshall-Olkin extended form Weibull distribution. As a result, there is only a small advantage for the likelihood ratio test

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In this work we study the accelerated failure-time generalized Gamma regression models with a unified approach. The models attempt to estimate simultaneously the effects of covariates on the acceleration/deceleration of the timing of a given event and the surviving fraction. The method is implemented in the free statistical software R. Finally the model is applied to a real dataset referring to the time until the return of the disease in patients diagnosed with breast cancer

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Survival models deals with the modeling of time to event data. However in some situations part of the population may be no longer subject to the event. Models that take this fact into account are called cure rate models. There are few studies about hypothesis tests in cure rate models. Recently a new test statistic, the gradient statistic, has been proposed. It shares the same asymptotic properties with the classic large sample tests, the likelihood ratio, score and Wald tests. Some simulation studies have been carried out to explore the behavior of the gradient statistic in fi nite samples and compare it with the classic statistics in diff erent models. The main objective of this work is to study and compare the performance of gradient test and likelihood ratio test in cure rate models. We first describe the models and present the main asymptotic properties of the tests. We perform a simulation study based on the promotion time model with Weibull distribution to assess the performance of the tests in finite samples. An application is presented to illustrate the studied concepts