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1.
Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies, and often more than one type of recurrent events is of interest. In this paper, we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covariates on the censored event processes of interest. An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions. We examine asymptotic properties of the proposed estimators. Finite sample properties of these estimators are demonstrated by simulations. 相似文献
2.
Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards... 相似文献
3.
Recurrent event data with multiple causes are often observed in biomedical studies. The additive hazards model describes a different aspect of the association between covariates and the failure time than does the proportional hazards model. In this paper, we introduce additive hazards models for the analysis of gap time data of recurrent events with multiple causes. We estimate the regression parameter vector and cumulative baseline cause specific hazard rate function using counting process approach. Asymptotic properties of the estimators are studied. The proposed model is applied to the kidney dialysis data given in Lawless (2003). A simulation study is carried out to assess the performance of the estimates. 相似文献
4.
Acta Mathematicae Applicatae Sinica, English Series - In the article, we investigate a general class of semiparametric hazards regression models for recurrent gap times. The general class includes... 相似文献
5.
Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided. 相似文献
6.
Huan-binLiu Liu-quanSun Li-xingZhu 《应用数学学报(英文版)》2005,21(2):237-246
Many survival studies record the times to two or more distinct failures on each subject. The failures may be events of different natures or may be repetitions of the same kind of event. In this article, we consider the regression analysis of such multivariate failure time data under the additive hazards model. Simple weighted estimating functions for the regression parameters are proposed, and asymptotic distribution theory of the resulting estimators are derived. In addition, a class of generalized Wald and generalized score statistics for hypothesis testing and model selection are presented, and the asymptotic properties of these statistics are examined. 相似文献
7.
The seminal Cox’s proportional intensity model with multiplicative frailty is a popular approach to analyzing the frequently
encountered recurrent event data in scientific studies. In the case of violating the proportional intensity assumption, the
additive intensity model is a useful alternative. Both the additive and proportional intensity models provide two principal
frameworks for studying the association between the risk factors and the disease recurrences. However, methodology development
on the additive intensity model with frailty is lacking, although would be valuable. In this paper, we propose an additive
intensity model with additive frailty to formulate the effects of possibly time-dependent covariates on recurrent events as
well as to evaluate the intra-class dependence within recurrent events which is captured by the frailty variable. The asymptotic
properties for both the regression parameters and the association parameters in frailty distribution are established. Furthermore,
we also investigate the large-sample properties of the estimator for the cumulative baseline intensity function. 相似文献
8.
本文对带相依终止事件的复发事件数据提出了一个联合建模分析方法,用一个带脆弱变量的可加可乘比率模型来刻画复发事件过程,还用带脆弱变量的Cox风险率模型来刻画终止事件过程,而且这两个过程的相依性由脆弱变量来刻画.我们利用估计方程的方法,对模型参数进行了估计,给出了所得估计的渐近性质.同时,通过数值模拟分析验证了估计的渐近性质.最后,利用该方法分析了弗吉尼亚大学慢性心脏病病人医疗诊费数据. 相似文献
9.
Somnath Datta Glen A. Satten John M. Williamson 《Annals of the Institute of Statistical Mathematics》2000,52(1):160-172
Satten et al. (1998, J. Amer. Statist. Assoc., 93, 318–327) proposed an approach to the proportional hazards model for interval censored data in which parameter estimates are obtained by solving estimating equations which are the score equations for the full data proportional hazards model, averaged over all rankings of imputed failure times consistent with the observed censoring intervals. In this paper, we extend this approach to incorporate data that are left-truncated and right censored (dynamic cohort data). Consistency and asymptotic normality of the estimators obtained in this way are established. 相似文献
10.
Lore Dirick Gerda Claeskens Bart Baesens 《The Journal of the Operational Research Society》2017,68(6):652-665
We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context. 相似文献
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12.
《Mathematical and Computer Modelling》1998,27(9-11):293-310
Forecasting traffic volume is an important task in controlling urban highways, guiding drivers' routes, and providing real-time transportation information. Previous research on traffic volume forecasting has concentrated on a single forecasting model and has reported positive results, which has been frequently better than those of other models. In addition, many previous researchers have claimed that neural network models are better than linear statistical models in terms of prediction accuracy. However, the forecasting power of a single model is limited to the typical cases to which the model fits best. In other words, even though many research efforts have claimed the general superiority of a single model over others in predicting future events, we believe it depends on the data characteristics used, the composition of the training data, the model architecture, and the algorithm itself.In this paper, we have studied the relationship in forecasting traffic volume between data characteristics and the forecasting accuracy of different models, particularly neural network models. To compare and test the forecasting accuracy of the models, three different data sets of traffic volume were collected from interstate highways, intercity highways, and urban intersections. The data sets show very different characteristics in terms of volatility, period, and fluctuations as measured by the Hurst exponent, the correlation dimension. The data sets were tested using a back-propagation network model, a FIR model, and a time-delayed recurrent model.The test results show that the time-delayed recurrent model outperforms other models in forecasting very randomly moving data described by a low Hurst exponent. In contrast, the FIR model shows better forecasting accuracy than the time-delayed recurrent network for relatively regular periodic data described by a high Hurst exponent. The interpretation of these results shows that the feedback mechanism of the previous error, through the temporal learning technique in the time-delayed recurrent network, naturally absorbs the dynamic change of any underlying nonlinear movement. The FIR and back-propagation model, which have claimed a nonlinear learning mechanism, may not be very good in handling randomly fluctuating events. 相似文献
13.
Length-biased data are often encountered in observational studies, when the survival times are left-truncated and right-censored and the truncation times follow a uniform distribution. In this article, we propose to analyze such data with the additive hazards model, which specifies that the hazard function is the sum of an arbitrary baseline hazard function and a regression function of covariates. We develop estimating equation approaches to estimate the regression parameters. The resultant estimators are shown to be consistent and asymptotically normal. Some simulation studies and a real data example are used to evaluate the finite sample properties of the proposed estimators. 相似文献
14.
B. García-Mora C. Santamaría G. Rubio 《Mathematical Methods in the Applied Sciences》2020,43(14):8302-8310
A methodology to model a process in which repeated events occur is presented. The context is the evolution of non-muscle-invasive bladder carcinoma (NMIBC), characterized by recurrent relapses. It is based on the statistical flowgraph approach, a technique specifically suited for semi-Markov processes. A very useful feature of the flowgraph framework is that it naturally incorporates the management of censored data. However, this approach presents two difficulties with the process to be modeled. On one hand, the management of covariates is not straightforward. However, it is of great interest to know how the characteristics of a certain patient influence the evolution of the disease. On the other hand, repeated events on the same subject are generally not independent, in which case the semi-Markov framework is not sufficient because the semi-Markov assumption implies independence among waiting time distributions. We solve this issue by extending the flowgraph methodology using the Markovian arrival process (MAP), which does successfully model the dependence between events. Along the way, we provide a procedure to consider covariates and censored times in MAPs, a pending task needed in this field. In short, we have managed to extend the flowgraph methodology beyond the semi-Markovian framework, simplifying the incorporation of covariates and keeping the management of censored times. All of which has allowed us to build a multistate model of the evolution of NMIBC. The developed model allows us to compute the Survival function for any evolution of a patient with specific clinic-pathological characteristics in this primary tumor. 相似文献
15.
WenBin Lu 《中国科学A辑(英文版)》2009,52(6):1169-1180
Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences
of an event of interest. In this paper, we evaluate two popular nonparametric tests for recurrent event time data in terms
of their relative efficiency. One is the log-rank test for classical survival data and the other a more recently developed
nonparametric test based on comparing mean recurrent rates. We show analytically that, somewhat surprisingly, the log-rank
test that only makes use of time to the first occurrence could be more efficient than the test for mean occurrence rates that
makes use of all available recurrence times, provided that subject-to-subject variation of recurrence times is large. Explicit
formula are derived for asymptotic relative efficiencies under the frailty model. The findings are demonstrated via extensive
simulations.
This work was supported by US National Science Foundation (Grant No. DMS-0504269) 相似文献
16.
R. Monneau M. Roussignol A. Tordeux 《NoDEA : Nonlinear Differential Equations and Applications》2014,21(4):491-517
In this paper we consider a microscopic model of traffic flow called the adaptive time gap car-following model. This is a system of ODEs which describes the interactions between cars moving on a single line. The time gap is the time that a car needs to reach the position of the car in front of it (if the car in front of it would not move and if the moving car would not change its velocity). In this model, both the velocity of the car and the time gap satisfy an ODE. We study this model and show that under certain assumptions, there is an invariant set for which the dynamics is defined for all times and for which we have a comparison principle. As a consequence, we show rigorously that after rescaling, this microscopic model converges to a macroscopic model that can be identified as the classical LWR model for traffic. 相似文献
17.
The latent class mixture-of-experts joint model is one of the important methods for jointly modelling longitudinal and recurrent events data when the underlying population is heterogeneous and there are nonnormally distributed outcomes. The maximum likelihood estimates of parameters in latent class joint model are generally obtained by the EM algorithm. The joint distances between subjects and initial classification of subjects under study are essential to finding good starting values of the EM algorithm through formulas. In this article, separate distances and joint distances of longitudinal markers and recurrent events are proposed for classification purposes, and performance of the initial classifications based on the proposed distances and random classification are compared in a simulation study and demonstrated in an example. 相似文献
18.
Additive hazards model with random effects is proposed for modelling the correlated failure time data when focus is on comparing the failure times within clusters and on estimating the correlation between failure times from the same cluster, as well as the marginal regression parameters. Our model features that, when marginalized over the random effect variable, it still enjoys the structure of the additive hazards model. We develop the estimating equations for inferring the regression parameters. The proposed estimators are shown to be consistent and asymptotically normal under appropriate regularity conditions. Furthermore, the estimator of the baseline hazards function is proposed and its asymptotic properties are also established. We propose a class of diagnostic methods to assess the overall fitting adequacy of the additive hazards model with random effects. We conduct simulation studies to evaluate the finite sample behaviors of the proposed estimators in various scenarios. Analysis of the Diabetic Retinopathy Study is provided as an illustration for the proposed method. 相似文献
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Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimation, the estimators utilizing external information can be more efficient. In this paper, we propose a method to incorporate external information into the estimation of the baseline hazard function and improve efficiency for estimating the absolute risk under the additive hazards model. The resulting estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Simulation studies demonstrate that the proposed method is much more efficient. An application to a bone marrow transplant data set is provided. 相似文献