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. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

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

Background/Objectives: 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. Methods: A retrospective study was performed on 60 consecutive T1b/T2 glottic cancer patients who underwent open frontolateral laryngectomy. The intraoperative margins were obtained from the tumor bed. Their recurrence and disease-free survival were evaluated. In all cases, negative margins were obtained from the surgical bed. The impact of positive margins from the specimen was evaluated in a paraffin study. Results: Among 10 patients with positive margins in the specimen, six experienced local relapse, and among 50 patients with negative margins in the specimen, three developed recurrence. The 5-year disease-free survival rates were 37.5% and 93.9%, respectively (p < 0.001; log-rank). Even with negative margins in the surgical bed, patients with positive margins in the specimen at the final histopathological examination had a 3.5-fold higher chance of developing local recurrence than those with negative margins (HR = 13.993; 95% CI: 3.479-56.281; p < 0.001; univariate Cox regression). Conclusions: Specimen-driven positive margins represent a significant risk factor for local recurrence, even under negative margins at the tumor bed.

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. Our proposal makes the PA dependent on the spatial lag. We establish the conditions for which the PA decays as a function of the distance lag for isotropic stationary and nonstationary spatial processes. Estimation is addressed through a first-order approximation that guarantees the asymptotic normality of the sample version of the PA. The sensitivity of the PA with respect to the covariance parameters is studied for finite sample size. The new method is described and illustrated with real data involving autumnal changes in the green chromatic coordinate (Gcc), an index of "greenness" that captures the phenological stage of tree leaves, is associated with carbon flux from ecosystems, and is estimated from repeated images of forest canopies.

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. METHODS: This is an exploratory cross-sectional study using an online questionnaire to be filled out on a voluntary basis among medical students and residents, consisting of: socio-demographic data, an empathy questionnaire and questions with elaborate clinical cases that typify situations of the variants of euthanasia. RESULTS: From 1550 invitations, 273 volunteer participants responded (17.6%). The percentages of strong agreement/agreement on the concepts were: passive euthanasia (72.9%); active euthanasia (22.3%), orthothanasia (90.1%), dysthanasia (18.7%), assisted suicide (33%) and sedation (82.8%). Passive euthanasia, active euthanasia, dysthanasia and assisted suicide showed greater refusal with increasing length of medical training. Religious belief and degree of empathy did not significantly influence the opinion about the concepts. Strong agreement/agreement were: passive euthanasia (72.9%); active euthanasia (22.3%), orthothanasia (90.1%), dysthanasia (18.7%), assisted suicide (33%) and sedation (82.8%). CONCLUSIONS: Passive euthanasia, active euthanasia, dysthanasia and assisted suicide showed greater refusal with increasing length of medical training. The external validation of our findings relies on the distinct legal, cultural, and religious frameworks found across various countries.

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. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.

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. Maximum likelihood estimation based on the Expectation Maximization algorithm is discussed. A simulation study designed to assess some properties of the estimators is carried out, showing the good performance of the proposed estimation procedure in finite samples. Finally, an application to a bone marrow transplant data set is presented.

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. The sample consisted of 46 patients with a diagnosis of head and neck cancer. Lymphedema was detected in 97.8% (45) of the evaluations with predominance of the composite type (73.9%-34). A high percentage of external lymphedema of the neck (71.7%-33) and submandibular (63%-29) were detected, with predominance of the more advanced levels. Internal edema was found in almost all structures and spaces at moderate/severe level. At FEES, residue (higher percentage in valleculae and pyriform sinus), penetration and aspirations were observed. The residue was detected in higher occurrence in patients with composite lymphedema (p = 0.012). The combined treatment with radiotherapy was related to submandibular external lymphedema (p = 0.009), altered pharyngolaryngeal sensitivity (0.040), presence of residue (p = 0.001) and penetration to pasty (p = 0.007) and internal edema in almost all structures. There was also a higher percentage of residue in cases with internal altered pharyngolaryngeal sensitivity, residue, penetration and aspiration. Combined treatment with radiotherapy is an associated factor of edema. Cervicofacial and pharyngolaryngeal lymphedema is a frequent event after treatment for HNC, with important impact on swallowing performance characterised by altered pharyngolaryngeal sensitivity, residue, penetration and aspiration. Combined treatment with radiotherapy is an associated factor.

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. In this article, we develop both Bayesian and frequentist approaches using a data-dependent Jeffreys-type prior to handle the monotone partial likelihood problem. We first carry out an in-depth examination of the conditions of the monotone partial likelihood and then characterize sufficient and necessary conditions for the propriety of the Jeffreys-type prior. We further study several theoretical properties of the Jeffreys-type prior for the Cox model. In addition, we propose two variations of the Jeffreys-type prior: the shifted Jeffreys-type prior and the Jeffreys-type prior based on the first risk set. An efficient Markov-chain Monte Carlo algorithm is developed to carry out posterior computation. We perform extensive simulations to examine the performance of parameter estimates and demonstrate the applicability of the proposed method by analyzing real data from the SEER prostate cancer study.

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. As a consequence, a general, simple, and effective method is presented, which does not require Markov Chain Monte Carlo methods and can be applied considering a great variety of prior distributions. Illustratively, the method was exemplified using five objective priors for the bivariate normal distribution. A tool for assessing the adequacy of the model is discussed. Results from a simulation study and an application to a real dataset are also reported.

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. RESULTS: 117 patients were studied, aged 12-89 years (mean 48), 70 women and 47 men. Thirty-nine patients (33.3%) were affected by inflammatory diseases (28 patients with lithiasis), 70 had benign tumors, and malignant tumors, eight. Regarding FNA, the sensitivity and specificity were 85.7% and 100%, respectively. Nine patients (7.7%) had temporary paralysis of the marginal mandibular nerve and one had permanent paralysis. CONCLUSION: resection of the submandibular gland is a safe procedure, with low complication rates.

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. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.

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. Under an improper prior for the parameters, we establish propriety of the posterior distribution. A novel Gibbs sampling algorithm is developed for sampling from the observed data posterior distribution. A simulation study is conducted to examine the properties of the proposed methodology. An application to a data set from a cord blood transplantation study is also reported.

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. Cox regression multivariable analysis was used to determine factors associated with death following PSA failure. RESULTS: After a median follow-up of 10.3 years of 108 men with PSA failure, 64 (59%) died, with 22 (34%) dying of prostate cancer (PC). Increasing PSA velocity at recurrence was associated with a significant increase in the risk of death (adjusted hazard ratio, 1.21; 95% confidence interval, 1.02-1.45; P = .03). Among men with no/minimal versus moderate/severe comorbidity, PC comprised 42% (20 of 48) versus 12.5% (2 of 16) of all deaths, respectively. Estimates of PC-specific and all-cause death were significantly higher when PSA velocity was greater than as compared with the median or less in men with no/minimal (P < .008) but not moderate/severe comorbidity (P > .15). CONCLUSIONS: Despite unfavorable PSA kinetics at recurrence, unhealthy men may not benefit from AST; RCTs examining intermittent AST versus surveillance are needed. For healthy men with unfavorable PSA kinetics at recurrence, PC death rates are high despite AST, which warrants RCTs to evaluate the impact on death when adding agents that prolong survival in men with metastatic castration-resistant PC to AST.

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. Thus, we assessed whether prostate-specific antigen (PSA) values can act as an early surrogate for prostate cancer-specific mortality (PCSM). METHODS: We systematically reviewed randomised controlled trials that showed improved overall and prostate cancer-specific survival with radiotherapy and 6 months of androgen suppression compared with radiotherapy alone and measured lowest PSA concentrations (PSA nadir) and those immediately after treatment (PSA end). We assessed a cohort of 734 men with localised or locally advanced prostate cancer from two eligible trials in the USA and Australasia that randomly allocated participants between Feb 2, 1996, and Dec 27, 2001. We used Prentice criteria to assess whether reported PSA nadir or PSA end concentrations of more than 0·5 ng/mL were surrogates for PCSM. FINDINGS: Men treated with radiotherapy and 6 months of androgen suppression in both trials were significantly less likely to have PSA end and PSA nadir values of more than 0·5 ng/mL than were those treated with radiotherapy alone (p<0·0001). Presence of candidate surrogates (ie, PSA end and PSA nadir values >0·5 ng/mL) alone and when assessed in conjunction with the randomised treatment group increased risk of PCSM in the US trial (PSA nadir p=0·0016; PSA end p=0·017) and Australasian trial (PSA nadir p<0·0001; PSA end p=0·0012). In both trials, the randomised treatment group was no longer associated with PCSM (p ≥ 0·20) when the candidate surrogates were included in the model. Therefore, both PSA metrics satisfied Prentice criteria for surrogacy. INTERPRETATION: After radiotherapy and 6 months of androgen suppression, men with PSA end values exceeding 0·5 ng/mL should be considered for long-term androgen suppression and those with localised or locally advanced prostate cancer with PSA nadir values exceeding 0·5 ng/mL should be considered for inclusion in randomised trials investigating the use of drugs that have extended survival in castration-resistant metastatic prostate cancer. FUNDING: None.

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 São Carlos, São 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 São Carlos, São Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Estudo do linfonodo cervical pela citologia do imprint / Cervical lymph node study by imprint cytology

Introdução: O estudo citológico do linfonodo pelo imprint éde grande importância para complementar o histopatológico,sendo superior ao obtido pela punção aspirativa por agulha fina.Método: Uma série consecutiva de 86 pacientes submetidos àressecção de linfonodos cervical com a preparação de imprintforam avaliados retrospectivamente. O diagnóstico final foideterminado pelo exame histopatológico do espécime excisado.Sensibilidade, especificidade, valores preditivos e acurácia foramavaliados. Resultados: Houve o diagnóstico final de 54 casos demalignidade, oito casos de tuberculose e 24 casos de hiperplasiareativa não específica. A sensibilidade, especificidade, valorespreditivos para os testes negativo e positivo e a acurácia do imprintforam 93,5%, 100%, 85,7%, 100% e 95,3%, respectivamente.Conclusão: O imprint positivo foi sempre coerente com odiagnóstico histopatológico final enquanto, com relação aosachados de negatividade, houve discordância 33% dos casos delinfoma não-Hodgkin e 25% nos casos de tuberculose ganglionar.Devido à sua alta acurácia, pode ser realizado como métodoauxiliar ao estudo histopatológico, que segue sendo o método-ouro.

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. In other words, what is recorded is only from the undamaged portion of the original number of risk factors.

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. 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.

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. 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.