915 resultados para Survival Analysis


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PURPOSE: To assess the Medical Subject Headings (MeSH) indexing of articles that employed time-to-event analyses to report outcomes of dental treatment in patients.

MATERIALS AND METHODS: Articles published in 2008 in 50 dental journals with the highest impact factors were hand searched to identify articles reporting dental treatment outcomes over time in human subjects with time-to-event statistics (included, n = 95), without time-to-event statistics (active controls, n = 91), and all other articles (passive controls, n = 6,769). The search was systematic (kappa 0.92 for screening, 0.86 for eligibility). Outcome-, statistic- and time-related MeSH were identified, and differences in allocation between groups were analyzed with chi-square and Fischer exact statistics.

RESULTS: The most frequently allocated MeSH for included and active control articles were "dental restoration failure" (77% and 52%, respectively) and "treatment outcome" (54% and 48%, respectively). Outcome MeSH was similar between these groups (86% and 77%, respectively) and significantly greater than passive controls (10%, P < .001). Significantly more statistical MeSH were allocated to the included articles than to the active or passive controls (67%, 15%, and 1%, respectively, P < .001). Sixty-nine included articles specifically used Kaplan-Meier or life table analyses, but only 42% (n = 29) were indexed as such. Significantly more time-related MeSH were allocated to the included than the active controls (92% and 79%, respectively, P = .02), or to the passive controls (22%, P < .001).

CONCLUSIONS: MeSH allocation within MEDLINE to time-to-event dental articles was inaccurate and inconsistent. Statistical MeSH were omitted from 30% of the included articles and incorrectly allocated to 15% of active controls. Such errors adversely impact search accuracy.

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Autologous stem cell transplantation (ASCT) consolidation remains the treatment of choice for patients with relapsed diffuse large B cell lymphoma. The impact of rituximab combined with chemotherapy in either first- or second-line therapy on the ultimate results of ASCT remains to be determined, however. This study was designed to evaluate the benefit of ASCT in patients achieving a second complete remission after salvage chemotherapy by retrospectively comparing the disease-free survival (DFS) after ASCT for each patient with the duration of the first complete remission (CR1). Between 1990 and 2005, a total of 470 patients who had undergone ASCT and reported to the European Blood and Bone Transplantation Registry with Medical Essential Data Form B information were evaluated. Of these 470 patients, 351 (74%) had not received rituximab before ASCT, and 119 (25%) had received rituximab before ASCT. The median duration of CR1 was 11 months. The median time from diagnosis to ASCT was 24 months. The BEAM protocol was the most frequently used conditioning regimen (67%). After ASCT, the 5-year overall survival was 63% (95% confidence interval, 58%-67%) and 5-year DFS was 48% (95% confidence interval, 43%-53%) for the entire patient population. Statistical analysis showed a significant increase in DFS after ASCT compared with duration of CR1 (median, 51 months versus 11 months; P < .001). This difference was also highly significant for patients with previous exposure to rituximab (median, 10 months versus not reached; P < .001) and for patients who had experienced relapse before 1 year (median, 6 months versus 47 months; P < .001). Our data indicate that ASCT can significantly increase DFS compared with the duration of CR1 in relapsed diffuse large B cell lymphoma and can alter the disease course even in patients with high-risk disease previously treated with rituximab.

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Patients with glioblastoma (GBM) have variable clinical courses, but the factors that underlie this heterogeneity are not understood. To determine whether the presence of the telomerase-independent alternative lengthening of telomeres (ALTs) mechanism is a significant prognostic factor for survival, we performed a retrospective analysis of 573 GBM patients. The presence of ALT was identified in paraffin sections using a combination of immunofluorescence for promyelocytic leukemia body and telomere fluorescence in situ hybridization. Alternative lengthening of telomere was present in 15% of the GBM patients. Patients with ALT had longer survival that was independent of age, surgery, and other treatments. Mutations in isocitrate dehydrogenase (IDH1mut) 1 frequently accompanied ALT, and in the presence of both molecular events, there was significantly longer overall survival. These data suggest that most ALT+ tumors may be less aggressive proneural GBMs, and the better prognosis may relate to the set of genetic changes associated with this tumor subtype. Despite improved overall survival of patients treated with the addition of chemotherapy to radiotherapy and surgery, ALT and chemotherapy independently provided a survival advantage, but these factors were not found to be additive. These results suggest a critical need for developing new therapies to target these specific GBM subtypes.

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Infiltration of cytotoxic T-lymphocytes in ovarian cancer is a favorable prognostic factor. Employing a differential expression approach, we have recently identified a number of genes associated with CD8+ T-cell infiltration in early stage ovarian tumors. In the present study, we validated by qPCR the expression of two genes encoding the transmembrane proteins GPC6 and TMEM132D in a cohort of early stage ovarian cancer patients. The expression of both genes correlated positively with the mRNA levels of CD8A, a marker of T-lymphocyte infiltration [Pearson coefficient: 0.427 (p = 0.0067) and 0.861 (p < 0.0001), resp.]. GPC6 and TMEM132D expression was also documented in a variety of ovarian cancer cell lines. Importantly, Kaplan-Meier survival analysis revealed that high mRNA levels of GPC6 and/or TMEM132D correlated significantly with increased overall survival of early stage ovarian cancer patients (p = 0.032). Thus, GPC6 and TMEM132D may serve as predictors of CD8+ T-lymphocyte infiltration and as favorable prognostic markers in early stage ovarian cancer with important consequences for diagnosis, prognosis, and tumor immunobattling.

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there has been much research on analyzing various forms of competing risks data. Nevertheless, there are several occasions in survival studies, where the existing models and methodologies are inadequate for the analysis competing risks data. ldentifiabilty problem and various types of and censoring induce more complications in the analysis of competing risks data than in classical survival analysis. Parametric models are not adequate for the analysis of competing risks data since the assumptions about the underlying lifetime distributions may not hold well. Motivated by this, in the present study. we develop some new inference procedures, which are completely distribution free for the analysis of competing risks data.

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Re-introduction is a technique widely used in the conservation of threatened bird species. With advances in aviculture the use of captive-produced individuals as the release stock is becoming more commonplace, and ideally, survival of captive-produced, released individuals should be no different from their wild-bred counterparts. During the late 1980s the Critically Endangered Mauritius kestrel (Falco punctatus) was successfully re-introduced into the Bambous mountain range, Mauritius, some 30 years after its local extinction. Between 1987 and 2001 the developing population was closely monitored enabling us to construct re-sighting histories for 88 released and 284 wild-bred kestrels. We used age-structured models in the survival analysis software program MARK to determine if an individual's origin influenced its subsequent survival. Our analysis indicated no compelling evidence for reduced survival among juvenile captive-reared and released individuals, relative to their wild-bred counterparts, across the majority of cohorts and only limited evidence of a cohort-specific effect. This study illustrates that despite the lack of a formal experimental approach it is still feasible to conduct an assessment of re-introduction outcomes and techniques.

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In survival analysis frailty is often used to model heterogeneity between individuals or correlation within clusters. Typically frailty is taken to be a continuous random effect, yielding a continuous mixture distribution for survival times. A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty. An MCMC approach is adopted and the deviance information criterion is used to compare models. As an illustration of the approach a bivariate data set of corneal graft survival times is analysed. (C) 2006 Elsevier B.V. All rights reserved.

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Re-introduction is a technique widely used in the conservation of threatened bird species. With advances in aviculture the use of captive-produced individuals as the release stock is becoming more commonplace, and ideally, survival of captive-produced, released individuals should be no different from their wild-bred counterparts. During the late 1980s the Critically Endangered Mauritius kestrel (Falco punctatus) was successfully re-introduced into the Bambous mountain range, Mauritius, some 30 years after its local extinction. Between 1987 and 2001 the developing population was closely monitored enabling us to construct re-sighting histories for 88 released and 284 wild-bred kestrels. We used age-structured models in the survival analysis software program MARK to determine if an individual's origin influenced its subsequent survival. Our analysis indicated no compelling evidence for reduced survival among juvenile captive-reared and released individuals, relative to their wild-bred counterparts, across the majority of cohorts and only limited evidence of a cohort-specific effect. This study illustrates that despite the lack of a formal experimental approach it is still feasible to conduct an assessment of re-introduction outcomes and techniques. (C) 2003 Elsevier Ltd. All rights reserved.

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In this paper we deal with a Bayesian analysis for right-censored survival data suitable for populations with a cure rate. We consider a cure rate model based on the negative binomial distribution, encompassing as a special case the promotion time cure model. Bayesian analysis is based on Markov chain Monte Carlo (MCMC) methods. We also present some discussion on model selection and an illustration with a real dataset.

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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.

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In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. in this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

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The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.