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.

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.

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 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.

Positive fluid balance is associated with reduced survival in critically ill patients with cancer

Background There are no studies that describe the impact of the cumulative fluid balance on the outcomes of cancer patients admitted to intensive care units ICUs. The aim of our study was to evaluate the relationship between fluid balance and clinical outcomes in these patients. Method One hundred twenty-two cancer patients were prospectively evaluated for survival during a 30-day period. Univariate (Chi-square, t-test, MannWhitney) and multiple logistic regression analyses were used to identify the admission parameters associated with mortality. Results The mean cumulative fluid balance was significantly higher in non-survivors than in survivors [1675?ml/24?h (4712921) vs. 887?ml/24?h (104557), P?=?0.017]. We used the area under the curve and the intersection of the sensibility and specificity curves to define a cumulative fluid balance value of 1100?ml/24?h. This value was used in the univariate model. In the multivariate model, the following variables were significantly associated with mortality in cancer patients: the Acute Physiology and Chronic Health Evaluation II score at admission [Odds ratio (OR) 1.15; 95% confidence interval (CI) (1.051.26), P?=?0.003], the Lung Injury Score at admission [OR 2.23; 95% CI (1.293.87), P?=?0.004] and a positive fluid balance higher than 1100?ml/24?h at ICU [OR 5.14; 95% CI (1.4518.24), P?=?0.011]. Conclusion A cumulative positive fluid balance higher than 1100?ml/24?h was independently associated with mortality in patients with cancer. These findings highlight the importance of improving the evaluation of these patients' volemic state and indicate that defined goals should be used to guide fluid therapy.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

Risk Factor Analysis of Late Survival After Heart Transplantation According to Donor Profile: A Multi-Institutional Retrospective Study of 512 Transplants

Introduction. Patients with terminal heart failure have increased more than the available organs leading to a high mortality rate on the waiting list. Use of Marginal and expanded criteria donors has increased due to the heart shortage. Objective. We analyzed all heart transplantations (HTx) in Sao Paulo state over 8 years for donor profile and recipient risk factors. Method. This multi-institutional review collected HTx data from all institutions in the state of Sao Paulo, Brazil. From 2002 to 2008 (6 years), only 512 (28.8%) of 1777 available heart donors were accepted for transplantation. All medical records were analyzed retrospectively; none of the used donors was excluded, even those considered to be nonstandard. Results. The hospital mortality rate was 27.9% (n = 143) and the average follow-up time was 29.4 +/- 28.4 months. The survival rate was 55.5% (n = 285) at 6 years after HTx. Univariate analysis showed the following factors to impact survival: age (P = .0004), arterial hypertension (P = .4620), norepinephrine (P = .0450), cardiac arrest (P = .8500), diabetes mellitus (P = .5120), infection (P = .1470), CKMB (creatine kinase MB) (P = .8694), creatinine (P = .7225), and Na+ (P = .3273). On multivariate analysis, only age showed significance; logistic regression showed a significant cut-off at 40 years: organs from donors older than 40 years showed a lower late survival rates (P = .0032). Conclusions. Donor age older than 40 years represents an important risk factor for survival after HTx. Neither donor gender nor norepinephrine use negatively affected early survival.

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.

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.

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.

Survival, Causes of Death, and Prognostic Factors in Systemic Sclerosis: Analysis of 947 Brazilian Patients

Objective. To analyze survival, prognostic factors, and causes of death in a large cohort of patients with systemic sclerosis (SSc). Methods. From 1991 to 2010, 947 patients with SSc were treated at 2 referral university centers in Brazil. Causes of death were considered SSc-related and non-SSc-related. Multiple logistic regression analysis was used to identify prognostic factors. Survival at 5 and 10 years was estimated using the Kaplan-Meier method. Results. One hundred sixty-eight patients died during the followup. Among the 110 deaths considered related to SSc, there was predominance of lung (48.1%) and heart (24.5%) involvement. Most of the 58 deaths not related to SSc were caused by infection, cardiovascular or cerebrovascular disease, and cancer. Male sex, modified Rodnan skin score (mRSS) > 20, osteoarticular involvement, lung involvement, and renal crisis were the main prognostic factors associated to death. Overall survival rate was 90% for 5 years and 84% for 10 years. Patients presented worse prognosis if they had diffuse SSc (85% vs 92% at 5 yrs, respectively, and 77% vs 87% at 10 yrs, compared to limited SSc), male sex (77% vs 90% at 5 yrs and 64% vs 86% at 10 yrs, compared to female sex), and mRSS > 20 (83% vs 90% at 5 yrs and 66% vs 86% at 10 yrs, compared to mRSS <20). Conclusion. Survival was worse in male patients with diffuse SSc, and lung and heart involvement represented the main causes of death in this South American series of patients with SSc. (First Release Aug 15 2012; J Rheumatol 2012;39:1971-8; doi:10.3899/jrheum.111582)

Risk Factors and Survival Impact of Primary Graft Dysfunction After Lung Transplantation in a Single Institution

Background. Lung transplantation has become a standard procedure for some end-stage lung diseases, but primary graft dysfunction (PGD) is an inherent problem that impacts early and late outcomes. The aim of this study was to define the incidence, risk factors, and impact of mechanical ventilation time on mortality rates among a retrospective cohort of lung transplantations performed in a single institution. Methods. We performed a retrospective study of 118 lung transplantations performed between January 2003 and July 2010. The most severe form of PGD (grade III) as defined at 48 and 72 hours was examined for risk factors by multivariable logistic regression models using donor, recipient, and transplant variables. Results. The overall incidence of PGD at 48 hours was 19.8%, and 15.4% at 72 hours. According multivariate analysis, risk factors associated with PGD were donor smoking history for 48 hours (adjusted odds ratio [OR], 4.83; 95% confidence interval [CI], 1.236-18.896; P = .022) and older donors for 72 hours (adjusted OR, 1.046; 95% CI, 0.997-1.098; P = .022). The operative mortality was 52.9% among patients with PGD versus 20.3% at 48 hours (P = .012). At 72 hours, the mortality rate was 58.3% versus 21.2% (P = .013). The 90-days mortality was also higher among patients with PGD. The mechanical ventilation time was longer in patients with PGD III at 48 hours namely, a mean time of 72 versus 24 hours (P = .001). When PGD was defined at 72 hours, the mean ventilation time was even longer, namely 151 versus 24 hours (P < .001). The mean overall survival for patients who developed PGD at 48 hours was 490.9 versus 1665.5 days for subjects without PGD (P = .001). Considering PGD only at 72 hours, the mean survival was 177.7 days for the PGD group and 1628.9 days for the other patients (P < .001). Conclusion. PGD showed an important impacts on operative and 90-day mortality rates, mechanical ventilation time, and overall survival among lung transplant patients. PGD at 72 hours was a better predictor of lung transplant outcomes than at 48 hours. The use of donors with a smoking history or of advanced age were risk factors for the development of PGD.

Prognostic factors for survival in patients with colorectal liver metastases: experience of a single brazilian cancer center

CONTEXT: Liver metastases are a common event in the clinical outcome of patients with colorectal cancer and account for 2/3 of deaths from this disease. There is considerable controversy among the data in the literature regarding the results of surgical treatment and prognostic factors of survival, and no analysis have been done in a large cohort of patients in Brazil. OBJECTIVES: To characterize the results of surgical treatment of patients with colorectal liver metastases, and to establish prognostic factors of survival in a Brazilian population. METHOD: This was a retrospective study of patients undergoing liver resection for colorectal metastases in a tertiary cancer hospital from 1998 to 2009. We analyzed epidemiologic variables and the clinical characteristics of primary tumors, metastatic disease and its treatment, surgical procedures and follow-up, and survival results. Survival analyzes were done by the Kaplan-Meier method and the log-rank test was applied to determine the influence of variables on overall and disease-free survival. All variables associated with survival with P<0.20 in univariate analysis, were included in multivariate analysis using a Cox proportional hazard regression model. RESULTS: During the period analyzed, 209 procedures were performed on 170 patients. Postope-rative mortality in 90 days was 2.9% and 5-year overall survival was 64.9%. Its independent prognostic factors were the presence of extrahepatic disease at diagnosis of liver metastases, bilateral nodules and the occurrence of major complications after liver surgery. The estimated 5-year disease-free survival was 39.1% and its prognostic factors included R1 resection, extrahepatic disease, bilateral nodules, lymph node involvement in the primary tumor and primary tumors located in the rectum. CONCLUSION: Liver resection for colorectal metastases is safe and effective and the analysis of prognostic factors of survival in a large cohort of Brazilian patients showed similar results to those pointed in international series. The occurrence of major postoperative complications appears to be able to compromise overall survival and further investigation in needed in this topic.

The relationship between lymphatic vascular density and vascular endothelial growth factor A (VEGF-A) expression with clinical-pathological features and survival in pancreatic adenocarcinomas

Abstract: Background Pancreatic cancer is a rare tumor with an extremely low survival rate. Its known risk factors include the chronic use of tobacco and excessive alcohol consumption and the presence of chronic inflammatory diseases, such as pancreatitis and type 2 diabetes. Angiogenesis and lymphangiogenesis, which have been the focus of recent research, are considered prognostic factors for cancer development. Knowing the angiogenic and lymphangiogenic profiles of a tumor may provide new insights for designing treatments according to the different properties of the tumor. The aim of this study was to evaluate the density of blood and lymphatic vessels, and the expression of VEGF-A, in pancreatic adenocarcinomas, as well as the relationship between blood and lymphatic vascular density and the prognostically important clinical-pathological features of pancreatic tumors. Methods Paraffin blocks containing tumor samples from 100 patients who were diagnosed with pancreatic cancer between 1990 and 2010 were used to construct a tissue microarray. VEGF expression was assessed in these samples by immunohistochemistry. To assess the lymphatic and vascular properties of the tumors, 63 cases that contained sufficient material were sectioned routinely. The sections were then stained with the D2-40 antibody to identify the lymphatic vessels and with a CD34 antibody to identify the blood vessels. The vessels were counted individually with the Leica Application Suite v4 program. All statistical analyses were performed using SPSS 18.0 (Chicago, IL, USA) software, and p values ≤ 0.05 were considered significant. Results In the Cox regression analysis, advanced age (p=0.03) and a history of type 2 diabetes (p=0.014) or chronic pancreatitis (p=0.02) were shown to be prognostic factors for pancreatic cancer. Blood vessel density (BVD) had no relationship with clinical-pathological features or death. Lymphatic vessel density (LVD) was inversely correlated with death (p=0.002), and by Kaplan-Meyer survival analysis, we found a significant association between low LVD (p=0.021), VEGF expression (p=0.023) and low patient survival. Conclusions Pancreatic carcinogenesis is related to a history of chronic inflammatory processes, such as type 2 diabetes and chronic pancreatitis. In pancreatic cancer development, lymphangiogenesis can be considered an early event that enables the dissemination of metastases. VEGF expression and low LVD can be considered as poor prognostic factors as tumors with this profile are fast growing and highly aggressive. Virtual slides. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/5113892881028514