Time to Left Ventricular Reverse Remodeling after Cardiac Resynchronization Therapy: Better Late than Never

INTRODUCTION: Left ventricular reverse remodeling (LVRR), defined as reduction of end-diastolic and end-systolic dimensions and improvement of ejection fraction, is associated with the prognostic implications of cardiac resynchronization therapy (CRT). The time course of LVRR remains poorly characterized. Nevertheless, it has been suggested that it occurs ≤6 months after CRT. OBJECTIVE: To characterize the long-term echocardiographic and clinical evolution of patients with LVRR occurring >6 months after CRT and to identify predictors of a delayed LVRR response. METHODS: A total of 127 consecutive patients after successful CRT implantation were divided into three groups according to LVRR response: Group A, 19 patients (15%) with LVRR after >6 months (late LVRR); Group B, 58 patients (46%) with LVRR before 6 months (early LVRR); and Group C, 50 patients (39%) without LVRR during follow-up (no LVRR). RESULTS: The late LVRR group was older, more often had ischemic etiology and fewer patients were in NYHA class ≤II. Overall, group A presented LVRR between group B and C. This was also the case with the percentage of clinical response (68.4% vs. 94.8% vs. 38.3%, respectively, p<0.001), and hospital readmissions due to decompensated heart failure (31.6% vs. 12.1% vs. 57.1%, respectively, p<0.001). Ischemic etiology (OR 0.044; p=0.013) and NYHA functional class

Complete longitudinal analyses of the randomized, placebo-controlled, phase III trial of sunitinib in patients with gastrointestinal stromal tumor following imatinib failure.

PURPOSE: To analyze final long-term survival and clinical outcomes from the randomized phase III study of sunitinib in gastrointestinal stromal tumor patients after imatinib failure; to assess correlative angiogenesis biomarkers with patient outcomes. EXPERIMENTAL DESIGN: Blinded sunitinib or placebo was given daily on a 4-week-on/2-week-off treatment schedule. Placebo-assigned patients could cross over to sunitinib at disease progression/study unblinding. Overall survival (OS) was analyzed using conventional statistical methods and the rank-preserving structural failure time (RPSFT) method to explore cross-over impact. Circulating levels of angiogenesis biomarkers were analyzed. RESULTS: In total, 243 patients were randomized to receive sunitinib and 118 to placebo, 103 of whom crossed over to open-label sunitinib. Conventional statistical analysis showed that OS converged in the sunitinib and placebo arms (median 72.7 vs. 64.9 weeks; HR, 0.876; P = 0.306) as expected, given the cross-over design. RPSFT analysis estimated median OS for placebo of 39.0 weeks (HR, 0.505, 95% CI, 0.262-1.134; P = 0.306). No new safety concerns emerged with extended sunitinib treatment. No consistent associations were found between the pharmacodynamics of angiogenesis-related plasma proteins during sunitinib treatment and clinical outcome. CONCLUSIONS: The cross-over design provided evidence of sunitinib clinical benefit based on prolonged time to tumor progression during the double-blind phase of this trial. As expected, following cross-over, there was no statistical difference in OS. RPSFT analysis modeled the absence of cross-over, estimating a substantial sunitinib OS benefit relative to placebo. Long-term sunitinib treatment was tolerated without new adverse events.

Estimação Bayesiana dos parâmetros da distribuição Exponencial Generalizada Bivariada

Pós-graduação em Matematica Aplicada e Computacional - FCT

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons.

We study the dynamical states of a small-world network of recurrently coupled excitable neurons, through both numerical and analytical methods. The dynamics of this system depend mostly on both the number of long-range connections or ?shortcuts?, and the delay associated with neuronal interactions. We find that persistent activity emerges at low density of shortcuts, and that the system undergoes a transition to failure as their density reaches a critical value. The state of persistent activity below this transition consists of multiple stable periodic attractors, whose number increases at least as fast as the number of neurons in the network. At large shortcut density and for long enough delays the network dynamics exhibit exceedingly long chaotic transients, whose failure times follow a stretched exponential distribution. We show that this functional form arises for the ensemble-averaged activity if the failure time for each individual network realization is exponen- tially distributed

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.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.

Appropriateness and long-term discontinuation rate of biological therapies in ulcerative colitis.

BACKGROUND: Anti-TNFα agents are commonly used for ulcerative colitis (UC) therapy in the event of non-response to conventional strategies or as colon-salvaging therapy. The objectives were to assess the appropriateness of biological therapies for UC patients and to study treatment discontinuation over time, according to appropriateness of treatment, as a measure of outcome. METHODS: We selected adult ulcerative colitis patients from the Swiss IBD cohort who had been treated with anti-TNFα agents. Appropriateness of the first-line anti-TNFα treatment was assessed using detailed criteria developed during the European Panel on the Appropriateness of Therapy for UC. Treatment discontinuation as an outcome was assessed for categories of appropriateness. RESULTS: Appropriateness of the first-line biological treatment was determined in 186 UC patients. For 64% of them, this treatment was considered appropriate. During follow-up, 37% of all patients discontinued biological treatment, 17% specifically because of failure. Time-to-failure of treatment was significantly different among patients on an appropriate biological treatment compared to those for whom the treatment was considered not appropriate (p=0.0007). Discontinuation rate after 2years was 26% compared to 54% between those two groups. Patients on inappropriate biological treatment were more likely to have severe disease, concomitant steroids and/or immunomodulators. They were also consistently more likely to suffer a failure of efficacy and to stop therapy during follow-up. CONCLUSION: Appropriateness of first-line anti-TNFα therapy results in a greater likelihood of continuing with the therapy. In situations where biological treatment is uncertain or inappropriate, physicians should consider other options instead of prescribing anti-TNFα agents.

Plasminogen activator inhibitor-1 activity and 4G/5G polymorphism in hemodialysis

Introduction: Chronic insufficiency alters homeostasis, in part due to endothelial inflammation. Plasminogen activator inhibitor-1 (PAI-1) is increased in renal disease, contributing to vascular damage. We assessed PAI-1 activity and PAI-1 4G/5G polymorphism in hemodialysis (HD) subjects and any association between thrombotic vascular access (VA) events and PAI-1 polymorphism. Methods: Prospective, observational study in 36 HD patients: mean age: 66.6 +/- 12.5 yr, males n=26 (72%), time on HD: 28.71 +/- 22.45 months. Vascular accesses: 10 polytetrafluoroethylene grafts (PTFEG), 22 arteriovenous fistulae (AVF), four dual lumen catheters (CAT). Control group (CG): 40 subjects; mean age: 60.0 +/- 15 yrs, males n=30 (75%). Group A (GA): thrombotic events (n=12), and group B (GB): No events (n=24). Groups were no different according to age (69.2 +/- 9.12 vs. 65.3 +/- 14.5 yrs), gender (males: 7; 58.3% vs. 18; 81.8%), time on HD (26.1 +/- 14.7 vs. 30.1 +/- 38.7 months), causes of renal failure. Time to follow-up, for access thrombosis: 12 months. Results: PAI-1 levels in HD: 7.21 +/- 2.13 vs. CG: 0.42 +/- 0.27 U/ml (p < 0.000 1). PAI-1 4G/5G polymorphic variant distribution in HD: 5G/5G: 6 (17%),4G/5G: 23 (64%); 4G/4G: 7 (19%) and in CG: 5G/5G: 14 (35%); 4G/5G: 18 (45%); 4G/4G: 8 (20%). C-reactive protein (CRP) in HD: 24.5 +/- 15.2 mg/L vs. in CG 2.3 +/- 0.2 mg/L (p < 0.0001). PAI-1 4G/5G variants: GA: 5G/5G: 3; 4G/5G: 8; 4G/4G: 1; GB: 5G/5G: 3; 4G/5G: 15; 4G/4G: 6. Thrombosis occurred in 8/10 patients (80%) with PTFEG, 3/22 (9%) in AVF, and 1/4 (25%) in CAT. Among the eight PTFEG patients with thrombosis, seven were PAI 4G/5G. Conclusions: PAI-1 levels were elevated in HD patients, independent of their polymorphic variants, 4G/5G being the most prevalent variant. Our data suggest that in patients with PTFEG the 4G/5G variant might be associated with an increased thrombosis risk.

Sequential designs for phase III clinical trials incorporating treatment selection

Most statistical methodology for phase III clinical trials focuses on the comparison of a single experimental treatment with a control. An increasing desire to reduce the time before regulatory approval of a new drug is sought has led to development of two-stage or sequential designs for trials that combine the definitive analysis associated with phase III with the treatment selection element of a phase II study. In this paper we consider a trial in which the most promising of a number of experimental treatments is selected at the first interim analysis. This considerably reduces the computational load associated with the construction of stopping boundaries compared to the approach proposed by Follman, Proschan and Geller (Biometrics 1994; 50: 325-336). The computational requirement does not exceed that for the sequential comparison of a single experimental treatment with a control. Existing methods are extended in two ways. First, the use of the efficient score as a test statistic makes the analysis of binary, normal or failure-time data, as well as adjustment for covariates or stratification straightforward. Second, the question of trial power is also considered, enabling the determination of sample size required to give specified power. Copyright © 2003 John Wiley & Sons, Ltd.

An adaptive group sequential design for phase II/III clinical trials that select a single treatment from several

There is increasing interest in combining Phases II and III of clinical development into a single trial in which one of a small number of competing experimental treatments is ultimately selected and where a valid comparison is made between this treatment and the control treatment. Such a trial usually proceeds in stages, with the least promising experimental treatments dropped as soon as possible. In this paper we present a highly flexible design that uses adaptive group sequential methodology to monitor an order statistic. By using this approach, it is possible to design a trial which can have any number of stages, begins with any number of experimental treatments, and permits any number of these to continue at any stage. The test statistic used is based upon efficient scores, so the method can be easily applied to binary, ordinal, failure time, or normally distributed outcomes. The method is illustrated with an example, and simulations are conducted to investigate its type I error rate and power under a range of scenarios.

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Modelagem de dados de falhas de equipamentos de sub-superfície em poços de petróleo da Bacia Potiguar

This master´s thesis presents a reliability study conducted among onshore oil fields in the Potiguar Basin (RN/CE) of Petrobras company, Brazil. The main study objective was to build a regression model to predict the risk of failures that impede production wells to function properly using the information of explanatory variables related to wells such as the elevation method, the amount of water produced in the well (BSW), the ratio gas-oil (RGO), the depth of the production bomb, the operational unit of the oil field, among others. The study was based on a retrospective sample of 603 oil columns from all that were functioning between 2000 and 2006. Statistical hypothesis tests under a Weibull regression model fitted to the failure data allowed the selection of some significant predictors in the set considered to explain the first failure time in the wells

Estimação em modelos de tempo de falha acelerado para dados de sobrevivência correlacionados

We presented in this work two methods of estimation for accelerated failure time models with random e_ects to process grouped survival data. The _rst method, which is implemented in software SAS, by NLMIXED procedure, uses an adapted Gauss-Hermite quadrature to determine marginalized likelihood. The second method, implemented in the free software R, is based on the method of penalized likelihood to estimate the parameters of the model. In the _rst case we describe the main theoretical aspects and, in the second, we briey presented the approach adopted with a simulation study to investigate the performance of the method. We realized implement the models using actual data on the time of operation of oil wells from the Potiguar Basin (RN / CE).

Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura

In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates