Association of the Specimen and Tumor Bed Margin Status with Local Recurrence and Survival in Open Partial Laryngectomy.

: Positive margins are associated with locoregional recurrence in early laryngeal cancer. The aim of this study was to evaluate the impacts of specimen-driven (ex vivo) positive margins on patients with early-stage laryngeal cancer whose tumor bed (defect-driven) margins had been negative. : A retrospective study was performed on 60 consecutive T1b/T2 glottic cancer patients who underwent open frontolateral laryngectomy.

Comparing two spatial variables with the probability of agreement.

Computing the agreement between 2 continuous sequences is of great interest in statistics when comparing 2 instruments or one instrument with a gold standard. The probability of agreement quantifies the similarity between 2 variables of interest, and it is useful for determining what constitutes a practically important difference. In this article, we introduce a generalization of the PA for the treatment of spatial variables.

Medical students' and residents' views on euthanasia.

Background: Doctors are increasingly faced with end-of-life decisions. Little is known about how medical students approach euthanasia. The objective of this study was to evaluate, among medical students and residents, the view on euthanasia and its variants; correlate such a view with empathy and religiosity/spiritualism; and with the stages of medical training in Brazil.

A new family of quantile regression models applied to nutritional data.

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization.

A Bayesian multi-risks survival (MRS) model in the presence of double censorings.

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data.

Correction to: Cervicofacial and Pharyngolaryngeal Lymphedema and Deglutition After Head and Neck Cancer Treatment.

The original version of this article unfortunately contained a mistake. Figure 2 was repeated in Figure 3.

On the use of the modified power series family of distributions in a cure rate model context.

In this paper, we propose a generalization of the power series cure rate model for the number of competing causes related to the occurrence of the event of interest. The model includes distributions not yet used in the cure rate models context, such as the Borel, Haight and Restricted Generalized Poisson distributions. The model is conveniently parameterized in terms of the cure rate.

Cervicofacial and Pharyngolaryngeal Lymphedema and Deglutition After Head and Neck Cancer Treatment.

One of the sequelae of head and neck cancer treatment is secondary lymphedema, with important impact on breathing, swallowing and vocal functions. The aim of the study was to assess the presence, staging characteristics and relationship of external and internal lymphedema and dysphagia after head and neck cancer treatment. The MDACC Lymphedema Rating Scale in Head and Neck Cancer was employed for the assessment and staging of face and neck lymphedema; the Radiotherapy Edema Scale for internal lymphedema; and a fiberoptic endoscopic evaluation of swallowing (FEES) for swallowing.

Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood.

In medical studies, the monotone partial likelihood is frequently encountered in the analysis of time-to-event data using the Cox model. For example, with a binary covariate, the subjects can be classified into two groups. If the event of interest does not occur (zero event) for all the subjects in one of the groups, the resulting partial likelihood is monotone and consequently, the covariate effects are difficult to estimate.

Bayesian inference for agreement measures.

The agreement of different measurement methods is an important issue in several disciplines like, for example, Medicine, Metrology, and Engineering. In this article, some agreement measures, common in the literature, were analyzed from a Bayesian point of view. Posterior inferences for such agreement measures were obtained based on well-known Bayesian inference procedures for the bivariate normal distribution.

Submandibular gland excision.

Objective: to analyze the value of fine needle aspiration and the rates of postoperative complications in patients undergoing resection of the submandibular gland.

Methods: we analyzed the records of patients treated with resection of the gland from January 1995 to December 2008. The data collected included age, gender, findings on clinical history, surgical procedure, results of fine needle aspiration (FNA), pathological diagnosis and complications.

Bayesian path specific frailty models for multi-state survival data with applications.

Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject.

Bayesian Transformation Models for Multivariate Survival Data.

In this paper we propose a general class of gamma frailty transformation models for multivariate survival data. The transformation class includes the commonly used proportional hazards and proportional odds models. The proposed class also includes a family of cure rate models.

Defining the optimal approach to the patient with postradiation prostate-specific antigen recurrence using outcome data from a prospective randomized trial.

Background: Optimal management remains unknown following prostate-specific antigen (PSA) failure when considering comorbidity and PSA kinetics at recurrence. In order to define randomized controlled trials (RCTs) that can address this issue, this study examined factors associated with the risk of death following PSA failure.

Methods: Of 206 men randomized to RT with or without 6 months of androgen suppression therapy (AST), 108 sustained PSA failure and began AST when PSA approached 10 ng/mL and formed the study cohort.

Surrogate endpoints for prostate cancer-specific mortality after radiotherapy and androgen suppression therapy in men with localised or locally advanced prostate cancer: an analysis of two randomised trials.

Background: Androgen suppression therapy and radiotherapy are used to treat locally advanced prostate cancer. 3 years of androgen suppression confers a small survival benefit compared with 6 months of therapy in this setting, but is associated with more toxic effects. Early identification of men in whom radiotherapy and 6 months of androgen suppression is insufficient for cure is important.

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.

Destructive weighted Poisson cure rate models.

In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow a compound weighted Poisson distribution. This model is more flexible in terms of dispersion than the promotion time cure model. Moreover, it gives an interesting and realistic interpretation of the biological mechanism of the occurrence of event of interest as it includes a destructive process of the initial risk factors in a competitive scenario.

[Prognostic factors and comorbidity impact upon the frontolateral laryngectomy].

Objective: To evaluate the survival rates, comorbidity impact, complications, and treatment failure facts.

Methods: 38 patients clinically staged as T1b / T2N0M0 glottic tumors were analyzed. They underwent frontolateral laryngectomy with reconstruction, from January, 1995 to December, 2006.

A hands-on approach for fitting long-term survival models under the GAMLSS framework.

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.

A Bayesian long-term survival model parametrized in the cured fraction.

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.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors.

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed.