THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called 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 these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

A general threshold stress hybrid hazard model for lifetime data

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set.

Overall Survival Improvement in Patients with Lung Cancer and Bone Metastases Treated with Denosumab Versus Zoledronic Acid Subgroup Analysis from a Randomized Phase 3 Study

Introduction: Denosumab, a fully human anti-RANKL monoclonal antibody, reduces the incidence of skeletal-related events in patients with bone metastases from solid tumors. We present survival data for the subset of patients with lung cancer, participating in the phase 3 trial of denosumab versus zoledronic acid (ZA) in the treatment of bone metastases from solid tumors (except breast or prostate) or multiple myeloma. Methods: Patients were randomized 1:1 to receive monthly subcutaneous denosumab 120 mg or intravenous ZA 4 mg. An exploratory analysis, using Kaplan-Meier estimates and proportional hazards models, was performed for overall survival among patients with non-small-cell lung cancer (NSCLC) and SCLC. Results: Denosumab was associated with improved median overall survival versus ZA in 811 patients with any lung cancer (8.9 versus 7.7 months; hazard ratio [HR] 0.80) and in 702 patients with NSCLC (9.5 versus 8.0 months; HR 0.78) (p = 0.01, each comparison). Further analysis of NSCLC by histological type showed a median survival of 8.6 months for denosumab versus 6.4 months for ZA in patients with squamous cell carcinoma (HR 0.68; p = 0.035). Incidence of overall adverse events was balanced between treatment groups; serious adverse events occurred in 66.0% of denosumab-treated patients and 72.9% of ZA-treated patients. Cumulative incidence of osteonecrosis of the jaw was similar between groups (0.7% denosumab versus 0.8% ZA). Hypocalcemia rates were 8.6% with denosumab and 3.8% with ZA. Conclusion: In this exploratory analysis, denosumab was associated with improved overall survival compared with ZA, in patients with metastatic lung cancer.

The log-exponentiated generalized gamma regression model for censored data

For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.

Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality

Background: Several models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting. Methodology: Survival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox's model and also of the Aalen's additive model. Principal Findings: One-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine, associated with an increased risk of dying, also presented an initial stronger effect. The impact of age and sodium were constant over time. Conclusions: The current study points to the importance of evaluating covariates with time-varying effects in heart failure models. The analysis performed suggests that variations of Cox and Aalen models constitute a valuable tool for identifying these variables. The implementation of covariates with time-varying effects into heart failure prognostication models may reduce bias and increase the specificity of such models.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

Home range, movements and diurnal roosts of the endangered thin-spined porcupine, Chaetomys subspinosus (Rodentia: Erethizontidae), in the Brazilian Atlantic Forest

Chaetomys subspinosus is the sole species within the Chaetomyinae subfamily of Caviomorph rodents. This poorly studied porcupine is restricted to the Atlantic Forest in eastern Brazil, where deforestation and habitat fragmentation threaten its survival. Data on the ranging and roosting behavior of C. subspinosus is fairly scarce as it is difficult to observe these behaviors in nature and, consequently, it is very rarely detected during field surveys. We monitored the home ranges of three radio-tagged females over the course of 1 year (2005-2006) and collected data on several aspects of their natural history including movement patterns and the use of diurnal roosts and latrines. The animals were monitored at Parque Estadual Paulo Cesar Vinha, a nature reserve dominated by restinga forests, a subtype of Atlantic Forest occurring on sandy soil. The estimated home range varied little between individuals and was relatively small (mean = 2.14 ha/individual and 1.09 ha/individual using minimum convex polygon and kernel methods, respectively). The animals travelled an average of 147 m/night (range: 21-324 m/night) between two consecutive day roosts. The day roosts were mostly located on vine and liana tangles in the canopy which also aid in connecting the canopy to adjacent trees or the forest floor. Latrines were mostly located near the ground in places heavily protected by spiny bromeliads or by other tangled vegetation. Our data suggests that C. subspinosus has the smallest range among all Neotropical Erethizontids which is likely due to its small size and strictly folivorous diet. Our data also helps explain why C. subspinosus is so difficult to observe in nature: researchers should focus on arboreal masses of tangled vegetation where individuals will normally rest during the day. (C) 2011 Deutsche Gesellschaft fur Saugetierkunde. Published by Elsevier GmbH. All rights reserved.

The geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area. (C) 2011 Elsevier B.V. All rights reserved.

A new long-term lifetime distribution induced by a latent complementary risk framework

In this paper, we proposed a new three-parameter long-term lifetime distribution induced by a latent complementary risk framework with decreasing, increasing and unimodal hazard function, the long-term complementary exponential geometric distribution. The new distribution arises from latent competing risk scenarios, where the lifetime associated scenario, with a particular risk, is not observable, rather we observe only the maximum lifetime value among all risks, and the presence of long-term survival. The properties of the proposed distribution are discussed, including its probability density function and explicit algebraic formulas for its reliability, hazard and quantile functions and order statistics. The parameter estimation is based on the usual maximum-likelihood approach. A simulation study assesses the performance of the estimation procedure. We compare the new distribution with its particular cases, as well as with the long-term Weibull distribution on three real data sets, observing its potential and competitiveness in comparison with some usual long-term lifetime distributions.

The negative binomial-beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial-beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.

Bevacizumab plus oxaliplatin-based chemotherapy as adjuvant treatment for colon cancer (AVANT): a phase 3 randomised controlled trial

Background: Bevacizumab improves the efficacy of oxaliplatin-based chemotherapy in metastatic colorectal cancer. Our aim was to assess the use of bevacizumab in combination with oxaliplatin-based chemotherapy in the adjuvant treatment of patients with resected stage III or high-risk stage II colon carcinoma. Methods: Patients from 330 centres in 34 countries were enrolled into this phase 3, open-label randomised trial. Patients with curatively resected stage III or high-risk stage II colon carcinoma were randomly assigned (1: 1: 1) to receive FOLFOX4 (oxaliplatin 85 mg/m(2), leucovorin 200 mg/m(2), and fluorouracil 400 mg/m(2) bolus plus 600 mg/m(2) 22-h continuous infusion on day 1; leucovorin 200 mg/m(2) plus fluorouracil 400 mg/m(2) bolus plus 600 mg/m(2) 22-h continuous infusion on day 2) every 2 weeks for 12 cycles; bevacizumab 5 mg/kg plus FOLFOX4 (every 2 weeks for 12 cycles) followed by bevacizumab monotherapy 7.5 mg/kg every 3 weeks (eight cycles over 24 weeks); or bevacizumab 7.5 mg/kg plus XELOX (oxaliplatin 130 mg/m(2) on day 1 every 2 weeks plus oral capecitabine 1000 mg/m(2) twice daily on days 1-15) every 3 weeks for eight cycles followed by bevacizumab monotherapy 7.5 mg/kg every 3 weeks (eight cycles over 24 weeks). Block randomisation was done with a central interactive computerised system, stratified by geographic region and disease stage. Surgery with curative intent occurred 4-8 weeks before randomisation. The primary endpoint was disease-free survival, analysed for all randomised patients with stage III disease. This study is registered with ClinicalTrials.gov, number NCT00112918. Findings: Of the total intention-to-treat population (n=3451), 2867 patients had stage III disease, of whom 955 were randomly assigned to receive FOLFOX4, 960 to receive bevacizumab-FOLFOX4, and 952 to receive bevacizumab-XELOX. After a median follow-up of 48 months (range 0-66 months), 237 patients (25%) in the FOLFOX4 group, 280 (29%) in the bevacizumab-FOLFOX4 group, and 253 (27%) in the bevacizumab-XELOX group had relapsed, developed a new colon cancer, or died. The disease-free survival hazard ratio for bevacizumab-FOLFOX4 versus FOLFOX4 was 1.17 (95% CI 0.98-1.39; p=0.07), and for bevacizumab-XELOX versus FOLFOX4 was 1.07 (0.90-1.28; p=0.44). After a minimum follow-up of 60 months, the overall survival hazard ratio for bevacizumab-FOLFOX4 versus FOLFOX4 was 1.27 (1.03-1.57; p=0.02), and for bevacizumab-XELOX versus FOLFOX4 was 1.15 (0.93-1.42; p=0.21). The 573 patients with high-risk stage II cancer were included in the safety analysis. The most common grade 3-5 adverse events were neutropenia (FOLFOX4: 477 [42%] of 1126 patients, bevacizumab-FOLFOX4: 416 [36%] of 1145 patients, and bevacizumab-XELOX: 74 [7%] of 1135 patients), diarrhoea (110 [10%], 135 [12%], and 181 [16%], respectively), and hypertension (12 [1%], 122 [11%], and 116 [10%], respectively). Serious adverse events were more common in the bevacizumab groups (bevacizumab-FOLFOX4: 297 [26%]; bevacizumab-XELOX: 284 [25%]) than in the FOLFOX4 group (226 [20%]). Treatment-related deaths were reported in one patient receiving FOLFOX4, two receiving bevacizumab-FOLFOX4, and five receiving bevacizumab-XELOX. Interpretation: Bevacizumab does not prolong disease-free survival when added to adjuvant chemotherapy in resected stage III colon cancer. Overall survival data suggest a potential detrimental effect with bevacizumab plus oxaliplatin-based adjuvant therapy in these patients. On the basis of these and other data, we do not recommend the use of bevacizumab in the adjuvant treatment of patients with curatively resected stage III colon cancer.

Xylella fastidiosa: An in vivo system to study possible survival strategies within citrus xylem vessels based on global gene expression analysis

Xylella fastidiosa inhabits the plant xylem, a nutrient-poor environment, so that mechanisms to sense and respond to adverse environmental conditions are extremely important for bacterial survival in the plant host. Although the complete genome sequences of different Xylella strains have been determined, little is known about stress responses and gene regulation in these organisms. In this work, a DNA microarray was constructed containing 2,600 ORFs identified in the genome sequencing project of Xylella fastidiosa 9a5c strain, and used to check global gene expression differences in the bacteria when it is infecting a symptomatic and a tolerant citrus tree. Different patterns of expression were found in each variety, suggesting that bacteria are responding differentially according to each plant xylem environment. The global gene expression profile was determined and several genes related to bacterial survival in stressed conditions were found to be differentially expressed between varieties, suggesting the involvement of different strategies for adaptation to the environment. The expression pattern of some genes related to the heat shock response, toxin and detoxification processes, adaptation to atypical conditions, repair systems as well as some regulatory genes are discussed in this paper. DNA microarray proved to be a powerful technique for global transcriptome analyses. This is one of the first studies of Xylella fastidiosa gene expression in vivo which helped to increase insight into stress responses and possible bacterial survival mechanisms in the nutrient-poor environment of xylem vessels.

A multiple time scale survival model with a cure fraction

Many recent survival studies propose modeling data with a cure fraction, i.e., data in which part of the population is not susceptible to the event of interest. This event may occur more than once for the same individual (recurrent event). We then have a scenario of recurrent event data in the presence of a cure fraction, which may appear in various areas such as oncology, finance, industries, among others. This paper proposes a multiple time scale survival model to analyze recurrent events using a cure fraction. The objective is analyzing the efficiency of certain interventions so that the studied event will not happen again in terms of covariates and censoring. All estimates were obtained using a sampling-based approach, which allows information to be input beforehand with lower computational effort. Simulations were done based on a clinical scenario in order to observe some frequentist properties of the estimation procedure in the presence of small and moderate sample sizes. An application of a well-known set of real mammary tumor data is provided.

Blood transfusion utilization and recipient survival at Hospital das Clinicas in Sao Paulo, Brazil

BACKGROUND: The characteristics of blood recipients including diagnoses associated with transfusion and posttransfusion survival are unreported in Brazil. The goals of this analysis were: 1) to describe blood utilization according to clinical diagnoses and patient characteristics and 2) to determine the factors associated with survival of blood recipients. STUDY DESIGN AND METHODS: A retrospective cross-sectional analysis was conducted on all inpatients in 2004. Data came from three sources: The first two files consist of data about patient characteristics, clinical diagnosis, and transfusion. Analyses comparing transfused and nontransfused patients were conducted. The third file was used to determine survival recipients up to 3 years after transfusion. Logistic regression was conducted among transfused patients to examine characteristics associated with survival. RESULTS: In 2004, a total of 30,779 patients were admitted, with 3835 (12.4%) transfused. These patients had 10,479 transfusions episodes, consisting of 39,561 transfused components: 16,748 (42%) red blood cells, 15,828 (40%) platelets (PLTs), and 6190 (16%) plasma. The median number of components transfused was three (range, 1-656) per patient admission. Mortality during hospitalization was different for patients whose admissions included transfusion or not (24% vs. 4%). After 1 year, 56% of transfusion recipients were alive. The multivariable model of factors associated with mortality after transfusion showed that the most significant factors in descending order were hospital ward, increasing age, increasing number of components transfused, and type of components received. CONCLUSION: Ward and transfusion are markers of underlying medical conditions and are associated with the probability of survival. PLT transfusions are common and likely reflect the types of patients treated. This comprehensive blood utilization study, the first of its kind in Brazil, can help in developing transfusion policy analyses in South America.