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.

COM-Poisson cure rate survival models and an application to a cutaneous melanoma data

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 the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. Next, we discuss the maximum likelihood estimation of the parameters of this cure rate survival model. Finally, we illustrate the usefulness of this model by applying it to a real cutaneous melanoma data. (C) 2009 Elsevier B.V. All rights reserved.

On the unification of long-term survival models

In this paper we extend the long-term survival model proposed by Chen et al. [Chen, M.-H., Ibrahim, J.G., Sinha, D., 1999. A new Bayesian model for survival data with a surviving fraction. journal of the American Statistical Association 94, 909-919] via the generating function of a real sequence introduced by Feller [Feller, W., 1968. An Introduction to Probability Theory and its Applications, third ed., vol. 1, Wiley, New York]. A direct consequence of this new formulation is the unification of the long-term survival models proposed by Berkson and Gage [Berkson, J., Gage, R.P., 1952. Survival cure for cancer patients following treatment. journal of the American Statistical Association 47, 501-515] and Chen et al. (see citation above). Also, we show that the long-term survival function formulated in this paper satisfies the proportional hazards property if, and only if, the number of competing causes related to the occurrence of an event of interest follows a Poisson distribution. Furthermore, a more flexible model than the one proposed by Yin and Ibrahim [Yin, G., Ibrahim, J.G., 2005. Cure rate models: A unified approach. The Canadian journal of Statistics 33, 559-570] is introduced and, motivated by Feller`s results, a very useful competing index is defined. (c) 2008 Elsevier B.V. All rights reserved.

A frailty modeling approach for parental effects in animal breeding

Survival models involving frailties are commonly applied in studies where correlated event time data arise due to natural or artificial clustering. In this paper we present an application of such models in the animal breeding field. Specifically, a mixed survival model with a multivariate correlated frailty term is proposed for the analysis of data from over 3611 Brazilian Nellore cattle. The primary aim is to evaluate parental genetic effects on the trait length in days that their progeny need to gain a commercially specified standard weight gain. This trait is not measured directly but can be estimated from growth data. Results point to the importance of genetic effects and suggest that these models constitute a valuable data analysis tool for beef cattle breeding.

Impact of number of vessels disease on outcome of patients with stable coronary artery disease: 5-year follow-up of the medical, angioplasty, and bypass surgery study (MASS)

Objective: To evaluate whether the number of vessels disease has an impact on clinical outcomes as well as on therapeutic results accordingly to medical, percutaneous, or surgery treatment in chronic coronary artery disease. Methods: We evaluated 825 individuals enrolled in MASS study, a randomized study to compare treatment options for single or multivessel coronary artery disease with preserved left ventricular function, prospectively followed during 5 years. The incidence of overall mortality and the composite end-point of death, myocardial infarction, and refractory angina were compared in three groups: single vessel disease (SVD n = 214), two-vessel disease (2VD n = 253) and three-vessel disease (3VD n = 358). The relationship between baseline variables and the composite end-point was assessed using a Cox proportional hazards survival model. Results: Most baseline characteristics were similar among groups, except age (younger in SVD and older in 3VD, p < 0.001), lower incidence of hypertension in SVD (p < 0.0001), and lower levels of total and LDL-cholesterol in 3VD (p = 0.004 and p = 0.005, respectively). There were no statistical differences in composite end-point in 5 years among groups independent of the kind of treatment; however, there was a higher mortality rate in 3VD (p < 0.001). When we stratified our analysis for each treatment option, bypass surgery was associated with a tower number of composite end-point in all groups (SVD p < 0.001, 2VD p = 0.002, 3VD p < 0.001). In multivariate analysis, we found higher mortality risk in 3VD comparing to SVD (p = 0.005, HR 3.14, 95%Cl 1.4-7.0). Conclusion: Three-vessel disease was associated with worse prognosis compared to single-or two-vessel disease in patients with stable coronary disease and preserved ventricular function at 5-year follow-up. In addition, event-free survival rates were higher after bypass surgery, independent of the number of vessels diseased in these subsets of patients. (c) 2008 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

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.

Bronchus-Associated Lymphoid Tissue (BALT) and Survival in a Vaccine Mouse Model of Tularemia

Background: Francisella tularensis causes severe pulmonary disease, and nasal vaccination could be the ideal measure to effectively prevent it. Nevertheless, the efficacy of this type of vaccine is influenced by the lack of an effective mucosal adjuvant. Methodology/Principal Findings: Mice were immunized via the nasal route with lipopolysaccharide isolated from F. tularensis and neisserial recombinant PorB as an adjuvant candidate. Then, mice were challenged via the same route with the F. tularensis attenuated live vaccine strain (LVS). Mouse survival and analysis of a number of immune parameters were conducted following intranasal challenge. Vaccination induced a systemic antibody response and 70% of mice were protected from challenge as showed by their improved survival and weight regain. Lungs from mice recovering from infection presented prominent lymphoid aggregates in peribronchial and perivascular areas, consistent with the location of bronchus-associated lymphoid tissue (BALT). BALT areas contained proliferating B and T cells, germinal centers, T cell infiltrates, dendritic cells (DCs). We also observed local production of antibody generating cells and homeostatic chemokines in BALT areas. Conclusions: These data indicate that PorB might be an optimal adjuvant candidate for improving the protective effect of F. tularensis antigens. The presence of BALT induced after intranasal challenge in vaccinated mice might play a role in regulation of local immunity and long-term protection, but more work is needed to elucidate mechanisms that lead to its formation.

Intracellular mechanisms of specific beta-adrenoceptor antagonists involved in improved cardiac function and survival in a genetic model of heart failure

beta-blockers, as class, improve cardiac function and survival in heart failure (HF). However, the molecular mechanisms underlying these beneficial effects remain elusive. In the present study, metoprolol and carvedilol were used in doses that display comparable heart rate reduction to assess their beneficial effects in a genetic model of sympathetic hyperactivity-induced HF (alpha(2A)/alpha(2C)-ARKO mice). Five month-old HF mice were randomly assigned to receive either saline, metoprolol or carvedilol for 8 weeks and age-matched wild-type mice (WT) were used as controls. HF mice displayed baseline tachycardia, systolic dysfunction evaluated by echocardiography, 50% mortality rate, increased cardiac myocyte width (50%) and ventricular fibrosis (3-fold) compared with WT. All these responses were significantly improved by both treatments. Cardiomyocytes from HF mice showed reduced peak [Ca(2+)](i) transient (13%) using confocal microscopy imaging. Interestingly, while metoprolol improved [Ca(2+)](i) transient, carvedilol had no effect on peak [Ca(2+)](i) transient but also increased [Ca(2+)] transient decay dynamics. We then examined the influence of carvedilol in cardiac oxidative stress as an alternative target to explain its beneficial effects. Indeed, HF mice showed 10-fold decrease in cardiac reduced/oxidized glutathione ratio compared with WT, which was significantly improved only by carvedilol treatment. Taken together, we provide direct evidence that the beneficial effects of metoprolol were mainly associated with improved cardiac Ca(2+) transients and the net balance of cardiac Ca(2+) handling proteins while carvedilol preferentially improved cardiac redox state. (C) 2008 Elsevier Inc. All rights reserved.

HUMAN CHOLESTERYL ESTER TRANSFER PROTEIN EXPRESSION ENHANCES THE MOUSE SURVIVAL RATE IN AN EXPERIMENTAL SYSTEMIC INFLAMMATION MODEL: A NOVEL ROLE FOR CETP

Mice expressing human cholesteryl ester transfer protein (huCETP) are more resistant to Escherichia coli bacterial wall LIPS because death rates 5 days after intraperitoneal inoculation of LIPS were higher in wild-type than in huCETP(+/-) mice, whereas all huCETP(+/+) mice remained alive. After LIPS inoculation, plasma concentrations of TNF-alpha and IL-6 increased less in huCETP(+/+) than in wild-type mice. LPS in vitro elicited lower TNF-alpha production by CETP expressing than by wild-type macrophages. In addition, TNF-alpha production by RAW 264.7 murine macrophages increased on incubation with LPS but decreased in a dose-dependent manner when human CETP was added to the medium. Human CETP in vitro enhanced the LIPS binding to plasma high-density lipoprotein/low-density lipoprotein. The liver uptake of intravenous infused C-14-LPS from Salmonella typhimurium was greater in huCETP(+/+) than in wild-type mice. Present data indicate for the first time that CETP is an endogenous component involved in the first line of defense against an exacerbated production of proinflammatory mediators.

A flexible model for survival data with a cure rate: a Bayesian approach

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.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Estimation of the mean quality-adjusted survival using a multistate model for the sojourn times

In clinical trials, it may be of interest taking into account physical and emotional well-being in addition to survival when comparing treatments. Quality-adjusted survival time has the advantage of incorporating information about both survival time and quality-of-life. In this paper, we discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models for the sojourn times in health states. Semiparametric and parametric (with exponential distribution) approaches are considered. A simulation study is presented to evaluate the performance of the proposed estimator and the jackknife resampling method is used to compute bias and variance of the estimator. (C) 2007 Elsevier B.V. All rights reserved.

Immune cells and oxidative stress in the endotoxin tolerance mouse model

Sepsis is a systemic inflammatory response that can lead to tissue damage and death. In order to increase our understanding of sepsis, experimental models are needed that produce relevant immune and inflammatory responses during a septic event. We describe a lipopolysaccharide tolerance mouse model to characterize the cellular and molecular alterations of immune cells during sepsis. The model presents a typical lipopolysaccharide tolerance pattern in which tolerance is related to decreased production and secretion of cytokines after a subsequent exposure to a lethal dose of lipopolysaccharide. The initial lipopolysaccharide exposure also altered the expression patterns of cytokines and was followed by an 8- and a 1.5-fold increase in the T helper 1 and 2 cell subpopulations. Behavioral data indicate a decrease in spontaneous activity and an increase in body temperature following exposure to lipopolysaccharide. In contrast, tolerant animals maintained production of reactive oxygen species and nitric oxide when terminally challenged by cecal ligation and puncture (CLP). Survival study after CLP showed protection in tolerant compared to naive animals. Spleen mass increased in tolerant animals followed by increases of B lymphocytes and subpopulation Th1 cells. An increase in the number of stem cells was found in spleen and bone marrow. We also showed that administration of spleen or bone marrow cells from tolerant to naive animals transfers the acquired resistance status. In conclusion, lipopolysaccharide tolerance is a natural reprogramming of the immune system that increases the number of immune cells, particularly T helper 1 cells, and does not reduce oxidative stress.

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.