Worker demography in a large-colony, swarm-founding wasp

Neotropical swarm-founding wasps build nests enclosed in a covering envelope, which makes it difficult to count individual births and deaths. Thus, knowledge of worker demography is very limited for swarm-founding species compared with that for independent-founding species. In this study, we explored the worker demography of the swarm-founding wasp Polybia paulista, the colony size of which usually exceeds several thousand adults. We considered each wasp colony as an open-population and estimated the survival probability, recruitment rate, and population size of workers using the developments of the Cormack-Jolly-Seber model. We found that capture probability varied considerably among the workers, probably due to age polyethism and/or task specialization. The daily survival rate of workers was high (around 0.97) throughout the season and was not related to the phase of colony development. On the other hand, the recruitment rate ranged from 0 to 0.37, suggesting that worker production was substantially less important than worker survival in determining worker population fluctuations. When we compared survival rates among worker groups of one colony, the mean daily survival rate was lower for founding workers than for progeny workers and tended to be higher in progeny workers that emerged in winter. These differences in survivorship patterns among worker cohorts would be related to worker foraging activity and/or level of parasitism.

Prognostic Impact of MYCN, DDX1, TrkA, and TrkC Gene Transcripts Expression in Neuroblastoma

Background. The aims of this study were to define the mRNA expression profiles of MYCN, DDX1, TrkA, and TrkC in biopsy tumor samples from 64 Brazilian patients with neuroblastomas of different risk stages and to correlate altered expression with prognostic values. Procedure. Patients were retrospectively classified into low- (n = 11), intermediate- (n = 18), and high-risk (n = 35) groups using standard criteria. The mRNA levels of the above genes were measured by quantitative real-time polymerase chain reaction. Univariate analyses were performed and survival curves were plotted by the Kaplan-Meier method. Results. Of the 64 patients, 53% were female and 62.5% were older than 18 months. The 5-year overall survival (OS) for the entire cohort was 40.3%, with inferior median OS in patients identified in the intermediate- and high-risk groups. A significant difference in OS with respect to TrkA mRNA expression was found for the high-risk group vs. either the low- or intermediate-risk groups (P < 0.01, log rank test). Within the intermediate-risk group, neuroblastoma patients with positive TrkA mRNA expression had better clinical outcomes than patients with no TrkA transcript expression (P = 0.004). Another difference in OS was only found between the intermediate- and high-risk groups (P < 0.027, log rank test). No significant correlation of mRNA expression and survival outcome could be detected for the MYCN, DDX1. Conclusions. Positive expression of TrkA mRNA may be a clinically useful addition to the current risk classification system, allowing the identification of NB tumors with favorable prognosis. Pediatr Blood Cancer 2011; 56: 749-756. (c) 2010 Wiley-Liss, Inc.

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.

Survival time of random walk in random environment among soft obstacles

We consider a Random Walk in Random Environment (RWRE) moving in an i.i.d. random field of obstacles. When the particle hits an obstacle, it disappears with a positive probability. We obtain quenched and annealed bounds on the tails of the survival time in the general d-dimensional case. We then consider a simplified one-dimensional model (where transition probabilities and obstacles are independent and the RWRE only moves to neighbour sites), and obtain finer results for the tail of the survival time. In addition, we study also the ""mixed"" probability measures (quenched with respect to the obstacles and annealed with respect to the transition probabilities and vice-versa) and give results for tails of the survival time with respect to these probability measures. Further, we apply the same methods to obtain bounds for the tails of hitting times of Branching Random Walks in Random Environment (BRWRE).

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

Different Approaches for Modeling Grouped Survival Data: A Mango Tree Study

Interval-censored survival data, in which the event of interest is not observed exactly but is only known to occur within some time interval, occur very frequently. In some situations, event times might be censored into different, possibly overlapping intervals of variable widths; however, in other situations, information is available for all units at the same observed visit time. In the latter cases, interval-censored data are termed grouped survival data. Here we present alternative approaches for analyzing interval-censored data. We illustrate these techniques using a survival data set involving mango tree lifetimes. This study is an example of grouped survival data.

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

Maned wolf survival rate in central Brazil

Although many carnivores are of conservation concern, most are poorly studied. The maned wolf Chrysocyon brachyurus Illiger, 1811 is the largest South American canid with a broad distribution; however, the largest portion of its range is in the Brazilian Cerrado savannah, where due to intensive agricultural expansion, it is threatened by habitat loss. Maned wolf population trends are virtually unknown. We analyzed radio telemetry data from a 13-year study in Emas National Park, central Brazil, with Burnham`s live recapture/dead recovery models in the program MARK to obtain the first analytically sound estimate of the apparent maned wolf survival rate. We constructed 16 candidate models including variation in survival rate and resighting probability associated with an individual`s sex or age and year of study. Apparent adult survival rate throughout the study ranged from 0.28 (se=0.08) to 0.97 (se=0.06). There was no evidence for sex specificity but strong support for time variation. Model weights supported an age effect and the subadult survival rate was 0.63 (se=0.15). Results indicate similar life patterns for male and female maned wolves and similar mortality risks for adults and subadults in the study area. The observed temporal fluctuations of adult survival rate are important for population dynamics as they decrease average population growth rates. Population dynamics are central for conservation planning and our results are an important step towards a better understanding of the maned wolf`s ecology.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

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.

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.

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.

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 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.