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.

A log-extended Weibull regression model

A bathtub-shaped failure rate function is very useful in survival analysis and reliability studies. The well-known lifetime distributions do not have this property. For the first time, we propose a location-scale regression model based on the logarithm of an extended Weibull distribution which has the ability to deal with bathtub-shaped failure rate functions. We use the method of maximum likelihood to estimate the model parameters and some inferential procedures are presented. We reanalyze a real data set under the new model and the log-modified Weibull regression model. We perform a model check based on martingale-type residuals and generated envelopes and the statistics AIC and BIC to select appropriate models. (C) 2009 Elsevier B.V. All rights reserved.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

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.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Evaluation of the role of environmental factors in the human gastrointestinal tract on the behaviour of probiotic cultures of Lactobacillus casei Shirota and Lactobacillus casei LC01 by the use of a semi-dynamic in vitro model

This study evaluated the influence of gastrointestinal environmental factors (pH, digestive enzymes, food components, medicaments) on the survival of Lactobacillus casei Shirota and Lactobacillus casei LC01, using a semi-dynamic in vitro model that simulates the transit of microorganisms through the human GIT. The strains were first exposed to different simulated gastric juices for different periods of time (0, 30, 60 and 120 min), and then to simulated intestinal fluids for zero, 120, 180 and 240 min, in a step-wise format. The number of viable cells was determined after each step. The influence of food residues (skim milk) in the fluids and resistance to medicaments commonly used for varied therapeutic purposes (analgesics, antiarrhythmics, antibiotics, antihistaminics, proton pump inhibitors, etc.) were also evaluated. Results indicated that survival of both cultures was pH and time dependent, and digestive enzymes had little influence. Milk components presented a protective effect, and medicaments, especially anti-inflammatory drugs, influenced markedly the viability of the probiotic cultures, indicating that the beneficial effects of the two probiotic cultures to health are dependent of environmental factors encountered in the human gastrointestinal tract.

Capacity of the extracellular matrix of the bone marrow to induce proliferation of myeloid cells in vitro in model of protein malnutrition in mice

The aim of this study was to verify the capacity of the extracellular matrix (ECM) obtained from bone marrow of malnourished mice to sustain survival and to induce the proliferation of myeloid cells. We also verified the capacity of the tests to interact with in vitro hematopoietic cytokines. Male ""Swiss"" mice were submitted to protein malnutrition with a diet contents of 4% casein until they lost 20% of the original weight, while the group-control was kept with a diet content of 14% of casein. The bone marrow was extracted with 1.0 mg of aprotinin/mL in PBS. The proliferation tests were carried out with myeloid cell line FDCP-1, by the colorimetric method of reduction of the MTT. The obtained ECM from nourished and undernourished mice induced cellular proliferation in vitro. Tests performed with Il-3 and GM-CSF cytokines in a concentration of 10 and 500 rho g/mL displayed synergic and regulatory effects respectively. The ECM obtained from the malnourished group submitted to the binding to GM-CSF demonstrated higher cellular proliferation than the ECM obtained from the control group (p<0.05). The results suggest that the alterations in the composition of ECM of bone marrow caused by malnutrition might lead to modification of the GM-CSF activity modulation.

Dexamethasone Effects in the Strongyloides venezuelensis Infection in A Murine Model

The aim of this study was to investigate the immunomodulatory effects of glucocorticoids on the immune response to Strongyloides venezuelensis in mice. Balb/c mice were infected with S. venezuelensis and treated with Dexamethasone (Dexa) or vehicle. Dexa treatment increased circulating blood neutrophil numbers and inhibited eosinophil and mononuclear cell accumulation in the blood, bronchoalveolar, and peritoneal fluid compared with control animals. Moreover, Dexa decreased tumor necrosis factor-alpha (TNF-alpha), interferon-gamma (IFN-gamma), interleukin-3 (IL-3), IL-4, IL-5, IL-10, and IL-12 production in the lungs and circulating immunoglobulin G1. (IgG1), IgG2a, and IgE antibody levels while increasing the overall parasite burden in the feces and intestine. Dexa treatment enhanced the fertility of female nematodes relative to untreated and infected mice. In summary, the alterations in the immune response induced by Dexa resulted in a blunted, aberrant immune response associated with increased parasite burden. This phenomenon is similar to that observed in S. stercoralis-infected humans who are taking immunosuppressive or antiinflammatory drugs, including corticosteroids.

Ameloblast apoptosis and IGF-1 receptor expression in the continuously erupting rat incisor model

Enamel-producing cells (ameloblasts) pass through several phenotypic and functional stages during enamel formation. In the transition between secretory and maturation stages, about one quarter of the ameloblasts suddenly undergo apoptosis. We have studied this phenomenon using the continuously erupting rat incisor model. A special feature of this model is that all stages of ameloblast differentiation are presented within a single longitudinal section of the developing tooth. This permits investigation of the temporal sequence of gene and growth factor receptor expression during ameloblast differentiation and apoptosis. We describe the light and electron microscopic morphology of ameloblast apoptosis and the pattern of insulin-like growth factor-1 receptor expression by ameloblasts in the continuously erupting rat incisor model. In the developing rat incisor, ameloblast apoptosis is associated with downregulated expression of the insulin-like growth factor-1 receptor. These data are consistent with the hypothesis that ameloblasts are hard wired for apoptosis and that insulin-like growth factor-1 receptor expression is required to block the default apoptotic pathway. Possible mechanisms of insulin-like growth factor-1 inhibition of ameloblast apoptosis are presented. The rat incisor model may be useful in studies of physiological apoptosis as it presents apoptosis in a predictable pattern in adult tissues.

A model for assembly and activation of the GM-CSF, IL-3 and IL-5 receptors: Insights from activated mutants of the common beta subunit

Granulocyte-macrophage colony stimulating factor (GM-CSF), Interleukin-3 (IL-3) and Interleukin-5 (IL-5) have overlapping, pleiotropic effects on hematopoietic cells, including neutrophils, eosinophils, monocytes and early progenitor cells. The high-affinity receptors for human GM-CSF, IL-3, and IL-5 share a common beta-subunit (h beta(c)), which is essential for signalling and plays a major role in recruiting intracellular signalling molecules. While activation of the cytoplasmic tyrosine kinase JAK2 appears to be the initiating event for signalling, the immediate events that trigger this are still unclear. We have isolated a number of activated mutants of h beta(c), which can be grouped into classes defined by their state of receptor phosphorylation, their requirement for alpha subunit as a cofactor, and their activities in primary cells and cell lines. We discuss these findings with regard to the stoichiometry, activation, and signalling of the normal GM-CSF/IL-3/IL-5 receptor complexes. Specifically, this work has implications for the role of the ligand-specific alpha-subunits in initiating the signalling through the beta-subunit, the role of beta subunit dimerization as a receptor trigger, and the function of receptor tyrosine phosphorylation in generating growth and survival signals. Based on the properties of the activated mutants and the recent structures of erythropoietin receptor (Epo-R) complexes, we propose a model in which (1) activation of h beta(c) can occur via alternative states that differ with respect to stoichiometry and subunit assembly, but which all mediate proliferative responses, and (2) each of the different classes of activated mutants mimics one of these alternative states. (C) 2000 International Society for Experimental Hematology. Published by Elsevier Science Inc.

A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk

Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.

Long-term survival of women with breast cancer in New South Wales

Several long-term studies of breast cancer survival have shown continued excess mortality from breast cancer up to 20-40 years following treatment. The purpose of this report was to investigate temporal trends in long-term survival from breast cancer in all New South Wales (NSW) women. Breast cancer cases incident in 1972-1996 (54,228) were derived from the NSW Central Cancer Registry a population-based registry which began in 1972. All cases of breast cancer not known to be dead were matched against death records. The expected survival for NSW women was derived from published annual life tables. Relative survival analysis compared the survival of cancer cases with the age, sex and period matched mortality of the total population. Cases were considered alive at the end of 1996, except when known to be dead. Proportional hazards regression was employed to model survival on age, period and degree of spread at diagnosis. Survival at 5, 10, 15, 20 and 25 years of follow-up was 76 per cent, 65 per cent, 60 per cent, 57 per cent and 56 per cent. The annual hazard rate for excess mortality was 4.3 per cent in year 1, maximal at 6.5 per cent in year 3, declining to 4.7 per cent in year 5, 2.7 per cent in year 10, 1.4 per cent in year 15, 1.0 per cent for years 16-20, and 0.4 per cent for years 20-25 of follow-up. Relative survival was highest in 40-49 year-olds. Cases diagnosed most recently (1992-1996) had the highest survival, compared with cases diagnosed in previous periods. Five-year survival improved over time, especially from the late 1980s for women in the screening age group (50-69 years). Survival was highest for those with localised cancer at diagnosis: 88.4 per cent, 79.1 per cent, 74.6 per cent, 72.7 per cent and 72.8 per cent at 5, 10, 15, 20 and 25 years follow-up (excluding those aged greater than or equal to 70 years). There was no significant difference between the survival of the breast cancer cases and the general population at 20-25 years follow-up. Degree of spread was less predictive of survival 5-20 years after diagnosis, compared with 0-5 years after diagnosis, and was not significant at 20-25 years of follow-up. Relative survival from breast cancer in NSW women continues to decrease to 25 years after diagnosis, but there is little excess mortality after 15 years follow-up, especially for those with localised cancer at diagnosis, and the minimal excess mortality at 20-25 years of follow-up is not statistically significant. (C) 2002 Elsevier Science Ltd. All rights reserved.

Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment

A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.