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.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone) .3. Determination of the time of pressure application

The curing temperature, pressure, and curing time have significant influence on finished thermosetting composite products. The time of pressure application is one of the most important processing parameters in the manufacture of a thermosetting composite. The determination of the time of pressure application relies on analysis of the viscosity variation of the polymer, associated with curing temperature and curing time. To determine it, the influence of the time of pressure application on the physical properties of epoxy-terminated poly(phenylene ether ketone) (E-PEK)-based continuous carbon fiber composite was studied. It was found that a stepwise temperature cure cycle is more suitable for manufacture of this composite. There are two viscosity valleys, in the case of the E-PEK system, associated with temperature during a stepwise cure cycle. The analysis on the effects of reinforcement fraction and defect content on the composite sheet quality indicates that the width-adjustable second viscosity valley provides a suitable pressing window. The viscosity, ranging from 400 to 1200 Pa . s at the second viscosity valley, is the optimal viscosity range for applying pressure to ensure appropriate resin flow during curing process, which enables one to get a finished composite with optimal fiber volume fraction and low void content. (C) 1997 John Wiley & Sons, Inc.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone). V: Estimation of temperature distribution during cure process

A numerical method to estimate temperature distribution during the cure of epoxy-terminated poly(phenylene ether ketone) (E-PEK)-based composite is suggested. The effect of the temperature distribution on the selection of cure cycle is evaluated using a suggested alternation criterion. The effect of varying heating rate and thickness on the temperature distribution, viscosity distribution and distribution of the extent of cure reaction are discussed based on the combination of the here-established temperature distribution model and the previously established curing kinetics model and chemorheological model. It is found that, for a thin composite (<=10mm) and low heating rate (<=2.5K/min), the effect of temperature distribution on cure cycle and on the processing window for pressure application can be neglected. Low heating rate is of benefit to reduce the temperature gradient. The processing window for pressure application becomes narrower with increasing thicknesses of composite sheets. The validity of the temperature distribution model and the modified processing window is evaluated through the characterization of mechanical and physical properties of E-PEK-based composite fabricated according to different temperature distribution conditions.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone) .4. Cure cycle simulation

Epoxy-terminated poly(phenylene ether ketone) (E-PEK) developed in this Institute is a candidate matrix resin for polymer composites as structural materials. Cure cycles for this reaction system were simulated according to the previously established processing model. It is found that for the E-PEK system, the curing process is best completed by a stepwise cure cycle comprising two isothermal processes at different temperatures, T-1 and T-2. The cure cycles over a wide range of processing parameters simulated, based on the established processing model, indicate that the processing window is width-adjustable. Analysis of the mechanical properties of the composite sheets showed that the simulated cure cycles are acceptable and reliable. (C) 1997 John Wiley & Sons, Inc.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone) .1. DSC characterization of curing reaction

The curing reaction process of epoxy-terminated poly(phenylene ether ketone) (E-PEK) with 4,4'-diaminodiphenyl sulfone (DDS) and hexahydrophthalic acid anhydride (Nadic) as curing agents was investigated using isothermal differential scanning calorimetry (IDSC) and nonisothermal differential scanning calorimetry (DDSC) techniques. It was found that the curing reactions of E-PEK/DDS and E-PEK/Nadic are nth-order reactions but not autoaccelerating. The experimental results revealed that the curing reaction kinetics parameters measured from IDSC and DDSC are not equivalent. This means that, in the curing reaction kinetics model for our E-PEK system, both isothermal and nonisothermal reaction kinetics parameters are needed to describe isothermal and nonisothermal curing processes, The isothermal and nonisothermal curing processes were successfully simulated using this model. A new extrapolation method was suggested. On the basis of this method the maximum extent of the curing reaction (A(ult)) that is able to reach a certain temperature can be predicted. The A(ult) for the E-PEK system estimated by the new method agrees well with the results obtained from another procedure reported in the literature. (C) 1997 John Wiley & Sons, Inc.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone) .2. Chemorheological modeling

Chemorheology and corresponding models for an epoxy-terminated poly(phenylene ether ketone) (E-PEK) and 4,4'-diaminodiphenyl sulfone (DDS) system were investigated using a differential scanning calorimeter (DSC) and a cone-and-plate rheometer. For this system, the reported four-parameter chemorheological model and modified WLF chemorheological model can only be used in an isothermal or nonisothermal process, respectively. In order to predict the resin viscosity variation during a stepwise temperature cure cycle actually used, a new model based on the combination of the four-parameter model and the modified WLF model was developed. The combined model can predict the resin viscosity variation during a stepwise temperature cure cycle more accurately than the above two models. In order to simplify the establishment of this model, a new five-parameter chemorheological model was then developed. The parameters in this five-parameter model can be determined through very few rheology and DSC experiments. This model is practicable to describe the resin viscosity variation for isothermal, nonisothermal, or stepwise temperature cure cycles accurately. The five-parameter chemorheological model has also successfully been used in the E-PEK systems with two other curing agents, i.e., the diamine curing agent with the addition of a boron trifluride monoethylamine (BF3-MEA) accelerator and an anhydride curing agent (hexahydrophthalic acid anhydride). (C) 1997 John Wiley & Sons, Inc.

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.

Monitoring of cure kinetic prepreg and cure cycle modeling

Cure kinetic model is an integral part of composite process simulation, which is used to predict the degree of curing and the amount of the generated heat. The parameters involved in kinetic models are usually determined empirically from isothermal or dynamic differential scanning calorimetry (DSC) data. In this work, DSC and rheological techniques were used to investigate some of the kinetic parameters of cure reactions of carbon/F161 epoxy prepreg and to evaluate the cure cycle used to manufacture polymeric composites for aeronautical applications. As a result, it was observed that the F161 prepreg presents cure kinetic with total order 1.2-1.9. (c) 2006 Springer Science + Business Media, Inc.

The Geometric Birnbaum-Saunders regression model with cure rate

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

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.

Is there a subgroup of long-term evolution among patients with advanced lung cancer?: Hints from the analysis of survival curves from cancer registry data

Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.

The Conway-Maxwell-Poisson-generalized gamma regression model with long-term survivors

In this paper, we proposed a flexible cure rate survival model by assuming the number of competing causes of the event of interest following the Conway-Maxwell distribution and the time for the event to follow the generalized gamma distribution. This distribution can be used to model survival data when the hazard rate function is increasing, decreasing, bathtub and unimodal-shaped including some distributions commonly used in lifetime analysis as particular cases. Some appropriate matrices are derived in order to evaluate local influence on the estimates of the parameters by considering different perturbations, and some global influence measurements are also investigated. Finally, data set from the medical area is analysed.

Modelo de tempo de falha acelerado com fração de cura : uma abordagem unificada

In this work we study the accelerated failure-time generalized Gamma regression models with a unified approach. The models attempt to estimate simultaneously the effects of covariates on the acceleration/deceleration of the timing of a given event and the surviving fraction. The method is implemented in the free statistical software R. Finally the model is applied to a real dataset referring to the time until the return of the disease in patients diagnosed with breast cancer

Uso de modelos estatísticos para dados de escore de crédito de uma instituição financeira

Pós-graduação em Engenharia de Produção - FEB