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1.
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
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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
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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
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In this paper, we investigate the quantile regression analysis for semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The estimation of quantile regression parameters for the non-terminal event is complicated. We cannot make inference on the non-terminal event without extra assumptions. Thus, we handle this problem by assuming that the joint distribution of the terminal event and the non-terminal event follows a parametric copula model with unspecified marginal distributions. We use the stochastic property of the martingale method to estimate the quantile regression parameters under semi-competing risks data. We also prove the large sample properties of the proposed estimator, and introduce a model diagnostic approach to check model adequacy. From simulation results, it shows that the proposed estimator performs well. For illustration, we apply our proposed approach to analyze a real data. 相似文献
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Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive Aalen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use. 相似文献
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Shuangge Ma 《Annals of the Institute of Statistical Mathematics》2011,63(1):117-134
Current status data arise when the exact timing of an event cannot be observed, and the only available information is whether or not the event has occurred at a random censoring time point. We consider current status data with a cured subgroup, where subjects in this subgroup are not susceptible to the event of interest. We model the cure probability using a generalized linear model with a known link function. For subjects susceptible to the event, we model their survival hazard using a partly linear additive risk model. We show that the penalized maximum likelihood estimate of the parametric regression coefficient is \({\sqrt{n}}\) consistent, asymptotically normal and efficient. The nonparametric cumulative baseline function and nonparametric covariate effect can be estimated with the n 1/3 convergence rate. We propose inference using the weighted bootstrap. Simulations study is employed to assess finite sample performance of the proposed estimate. We analyze the Calcification study using the proposed approach. 相似文献
8.
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
9.
A cured model is a useful approach for analysing failure
time data in which some subjects could eventually experience and others never
experience the event of interest. All subjects in the test belong to one of the
two groups: the susceptible group and the non-susceptible group. There has been
considerable progress in the development of semi-parametric models for regression
analysis of time-to-event data. However, most of the current work focuses on
right-censored data, especially when the population contains a non-ignorable
cured subgroup. In this paper, we propose a semi-parametric cure model for current
status data. In general, treatments are developed to both increase the patients'
chances of being cured and prolong the survival time among non-cured patients. A
logistic regression model is proposed for whether the subject is in the susceptible
group. An accelerated failure time regression model is proposed for the event
time when the subject is in the non-susceptible group. An EM algorithm is used
to maximize the log-likelihood of the observed data. Simulation results show that
the proposed method can get efficient estimations. 相似文献
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Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparametric mixed effect model with time-varying latent effects in the analysis of longitudinal data with informative observation times and a dependent terminal event. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is provided. 相似文献
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In this article, we propose a general additive-multiplicative rates model for recurrent event data. The proposed model includes the additive rates and multiplicative rates models as special cases. For the inference on the model parameters, estimating equation approaches are developed, and asymptotic properties of the proposed estimators are established through modern empirical process theory. In addition, an illustration with multiple-infection data from a clinic study on chronic granulomatous disease is pr... 相似文献
13.
Xiao-lin Chen 《应用数学学报(英文版)》2014,30(3):681-698
In [13], Schaubel et al. proposed a semiparametric partially linear rate model for the statistical analysis of recurrent event data. But they only considered the model with time-independent covariate effects. In this paper, rate function of the recurrent event is modeled by a semipaxametric partially linear function which can include the time-varying effects. We propose the method of generalized estimating equations to make inferences about both the time-varying effects and time-independent effects. The large sample properties are established, while extensive simulation studies are carried out to examine the proposed procedures. At last, we apply the procedures to the well-known bladder cancer study. 相似文献
14.
A model is introduced that describes a decrease in public attention to a past one-time political event, such as one-round elections, referendums, and coup d’état attempts. The number of web search queries is taken as an empirical measure of public attention to the event. The model is shown to match actual data. 相似文献
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In this paper, we propose a new non‐default rate survival model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes, which may be responsible for the occurrence of the event of interest, is assumed to follow a geometric distribution, while the time to event is assumed to follow an inverse Weibull distribution. An advantage of our approach is to accommodate all activation mechanisms based on order statistics. We explore the use of maximum likelihood estimation procedure. Simulation studies are performed and experimental results are illustrated based on a real Brazilian bank personal loan portfolio data. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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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
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In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event (survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer. 相似文献
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本文在成组复发事件下研究了一个一般半参数的边际变换模型,利用估计方程的理论,给出了该模型中未知参数和基本比率函数的估计,同时利用现代经验过程理论证明了所得估计的相合性和渐近正态性. 相似文献