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1.
《统计学通讯:理论与方法》2013,42(8):1771-1791
ABSTRACT This article considers degradation and failure time models with multiple failure modes which used to study the problem of longevity and aging in survival analysis and reliability. Degradation process is modeled using general nonparametric, nonlinear path models. Semi-parametric models for the intensities of the traumatic failures are used supposing that these intensities depend on degradation level. Semi-parametric estimators of various reliability characteristics are proposed and asymptotic properties of the estimators are obtained. The theoretical results are illustrated using simulated data. 相似文献
2.
In biomedical studies, correlated failure time data arise often. Although point and confidence interval estimation for quantiles with independent censored failure time data have been extensively studied, estimation for quantiles with correlated failure time data has not been developed. In this article, we propose a nonparametric estimation method for quantiles with correlated failure time data. We derive the asymptotic properties of the quantile estimator and propose confidence interval estimators based on the bootstrap and kernel smoothing methods. Simulation studies are carried out to investigate the finite sample properties of the proposed estimators. Finally, we illustrate the proposed method with a data set from a study of patients with otitis media. 相似文献
3.
This paper introduces a parametric discrete failure time model which allows a variety of smooth hazard function shapes, including shapes which are not readily available with continuous failure time models. The model is easy to fit, and statistical inference is simple. Further, it is readily extended to allow for differences between subjects while retaining the ease of fit and simplicity of statistical inference. The performance of the discrete time analysis is demonstrated by application to several data sets. 相似文献
4.
Rank Regression Analysis of Multivariate Failure Time Data Based on Marginal Linear Models 总被引:2,自引:0,他引:2
Abstract. Multivariate failure time data arises when each study subject can potentially ex-perience several types of failures or recurrences of a certain phenomenon, or when failure times are sampled in clusters. We formulate the marginal distributions of such multivariate data with semiparametric accelerated failure time models (i.e. linear regression models for log-transformed failure times with arbitrary error distributions) while leaving the dependence structures for related failure times completely unspecified. We develop rank-based monotone estimating functions for the regression parameters of these marginal models based on right-censored observations. The estimating equations can be easily solved via linear programming. The resultant estimators are consistent and asymptotically normal. The limiting covariance matrices can be readily estimated by a novel resampling approach, which does not involve non-parametric density estimation or evaluation of numerical derivatives. The proposed estimators represent consistent roots to the potentially non-monotone estimating equations based on weighted log-rank statistics. Simulation studies show that the new inference procedures perform well in small samples. Illustrations with real medical data are provided. 相似文献
5.
In this article, a simple and efficient weighted method is proposed to improve the estimation efficiency for the linear transformation models with multivariate failure time data. Asymptotic properties of the estimators with a closed-form variance-covariance matrix are established. In addition, a goodness-of-fit test is developed to evaluate the adequacy of the model. The performance of proposed method and the comparison on the efficiency between the proposed method and the working independence method (Lu, 2005) are conducted in finite-sample situation by simulation studies. Finally a real data set from the Busselton Population Health Surveys is illustrated to validate the proposed methodology. The related proofs of the theorems are given in the Appendix. 相似文献
6.
Clustered failure time data are commonly encountered in biomedical research where the study subjects from the same cluster (e.g., family) share the common genetic and/or environmental factors such that the failure times within the same cluster are correlated. Two approaches that are commonly used to account for the intra-cluster association are frailty models and marginal models. In this paper, we study the marginal proportional hazards model, where the structure of dependence between individuals within a cluster is unspecified. An estimation procedure is developed based on a pseudo-likelihood approach, and a risk set sampling method is proposed for the formulation of the pseudo-likelihood. The asymptotic properties of the proposed estimators are studied, and the related issues regarding the statistical efficiencies are discussed. The performances of the proposed estimator are demonstrated by the simulation studies. A data example from a child vitamin A supplementation trial in Nepal (Nepal Nutrition Intervention Project-Sarlahi, or NNIPS) is used to illustrate this methodology. 相似文献
7.
This paper considers comparison of discrete failure time distributions when the survival time of interest measures elapsed time between two related events and observations on the occurrences of both events could be interval-censored. This kind of data is often referred to as doubly interval-censored failure time data. If the occurrence of the first event defining the survival time can be exactly observed, the data are usually referred to as interval-censored data. For the comparison problem based on interval-censored failure time data, Sun (1996) proposed a nonparametric test procedure. In this paper we generalize the procedure given in Sun (1996) to doubly interval-censored data case and the generalized test is evaluated by simulations. 相似文献
8.
In dealing with ties in failure time data the mechanism by which the data are observed should be considered. If the data are discrete, the process is relatively simple and is determined by what is actually observed. With continuous data, ties are not supposed to occur, but they do because the data are grouped into intervals (even if only rounding intervals). In this case there is actually a non–identifiability problem which can only be resolved by modelling the process. Various reasonable modelling assumptions are investigated in this paper. They lead to better ways of dealing with ties between observed failure times and censoring times of different individuals. The current practice is to assume that the censoring times occur after all the failures with which they are tied. 相似文献
9.
Interval-censored data naturally arise in many studies. For their regression analysis, many approaches have been proposed under various models and for most of them, the inference is carried out based on the asymptotic normality. In particular, Zhang et al. (2005) discussed the procedure under the linear transformation model. It is well-known that the symmetric property implied by the normal distribution may not be appropriate sometimes. Also the method could underestimate the variance of estimated parameters. This paper proposes an empirical likelihood-based procedure for the problem. Simulation and the analysis of a real data set are conducted to assess the performance of the procedure. 相似文献
10.
Estimated Generalized Estimating Equation for Correlated Failure Time Data with Auxiliary Covariates
In this article, we study a marginal hazard model with common baseline hazard for correlated failure time data. We assume that the true covariate is measured precisely in a subset of the whole study cohort, whereas an auxiliary information for the true covariate is available for the whole cohort. We first estimate the relative risk function empirically. Then we obtain the estimator for the regression parameter by replacing the relative risk function with its estimator in a generalized estimating equation (GEE) proposed by Cai (1992). A key feature of this method is that it is nonparametric with respect to the association between the missing covariate and the observed auxiliary covariate. The proposed estimator is shown to be consistent and asymptotically normal. Furthermore, we present a corrected Breslow-type estimator for the cumulative hazard function. Simulation studies are conducted to evaluate the proposed method. 相似文献
11.
Man Jin 《统计学通讯:模拟与计算》2013,42(4):614-619
In randomized clinical trials or observational studies, subjects are recruited at multiple treating sites. Factors that vary across sites may have some influence on outcomes; therefore, they need to be taken into account to get better results. We apply the accelerated failure time (AFT) model with linear mixed effects to analyze failure time data, accounting for correlations between outcomes. Specifically, we use Bayesian approach to fit the data, computing the regression parameters by Gibbs sampler combined with Buckley-James method. This approach is compared with the marginal independence approach and other methods through simulations and an application to a real example. 相似文献
12.
Smoothed Gehan rank estimation methods are widely used in accelerated failure time (AFT) models with/without clusters. However, most methods are sensitive to outliers in the covariates. In order to solve this problem, we propose robust approaches based on the smoothed Gehan rank estimation methods for the AFT model, allowing for clusters by employing two different weight functions. Simulation studies show that the proposed methods outperform existing smoothed rank estimation methods regarding their biases and standard deviations when there are outliers in the covariates. The proposed methods are also applied to a real dataset from the “Major cardiovascular interventions” study. 相似文献
13.
Gray RJ 《Lifetime data analysis》2003,9(2):123-138
Estimation of regression parameters in linear survival models is considered in the clustered data setting. One step updates from an initial consistent estimator are proposed. The updates are based on scores that are functions of ranks of the residuals, and that incorporate weight matrices to improve efficiency. Optimal weights are approximated as the solution to a quadratic programming problem, and asymptotic relative efficiencies to various other weights computed. Except under strong dependence, simpler methods are found to be nearly as efficient as the optimal weights. The performance of several practical estimators based on exchangeable and independence working models is explored in simulations. 相似文献
14.
15.
Bernhard Klar 《Australian & New Zealand Journal of Statistics》1999,41(3):337-352
In recent years, a large number of new discrete distributions have appeared in the literature. However, flexible discrete models which, at the same time, allow for easy statistical inference, are still an exception. This paper makes a detailed analysis of a family of discrete failure time distributions which meets both requirements. It examines the maximum likelihood estimation of the unknown parameters and presents a goodness-of-fit test for this model. The test is used for the selection of an appropriate model for datasets of frequencies of the duration of atmospheric circulation patterns. 相似文献
16.
Regression Estimation Using Multivariate Failure Time Data and a Common Baseline Hazard Function Model 总被引:1,自引:1,他引:0
Recent ‘marginal’ methods for the regression analysis of multivariate failure time data have mostly assumed Cox (1972)model
hazard functions in which the members of the cluster have distinct baseline hazard functions. In some important applications,
including sibling family studies in genetic epidemiology and group randomized intervention trials, a common baseline hazard
assumption is more natural. Here we consider a weighted partial likelihood score equation for the estimation of regression
parameters under a common baseline hazard model, and provide corresponding asymptotic distribution theory. An extensive series
of simulation studies is used to examine the adequacy of the asymptotic distributional approximations, and especially the
efficiency gain due to weighting, as a function of strength of dependency within cluster, and cluster size.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
17.
In many reliability applications, there may not be a unique plausible scale in which to measure time to failure or assess performance. This is especially the case when several measures of usage are available on each unit. For example, the age, the total number of flight hours, and the number of landings are usage measures that are often considered important in aircraft reliability. Similarly, in medical or biological applications of survival analysis there are often alternative scales (e.g., Oakes, 1995). This paper considers the definition of a "good" time scale, along with methods of determining a time scale. 相似文献
18.
Manufacturers want to assess the quality andreliability of their products. Specifically, they want to knowthe exact number of failures from the sales transacted duringa particular month. Information available today is sometimesincomplete as many companies analyze their failure data simplycomparing sales for a total month from a particular departmentwith the total number of claims registered for that given month.This information—called marginal count data—is, thus,incomplete as it does not give the exact number of failures ofthe specific products that were sold in a particular month. Inthis paper we discuss nonparametric estimation of the mean numbersof failures for repairable products and the failure probabilitiesfor nonrepairable products. We present a nonhomogeneous Poissonprocess model for repairable products and a multinomial modeland its Poisson approximation for nonrepairable products. A numericalexample is given and a simulation is carried out to evaluatethe proposed methods of estimating failure probabilities undera number of possible situations. 相似文献
19.
Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure 总被引:7,自引:0,他引:7
The gamma process is a natural model for degradation processes in which deterioration is supposed to take place gradually over time in a sequence of tiny increments. When units or individuals are observed over time it is often apparent that they degrade at different rates, even though no differences in treatment or environment are present. Thus, in applying gamma-process models to such data, it is necessary to allow for such unexplained differences. In the present paper this is accomplished by constructing a tractable gamma-process model incorporating a random effect. The model is fitted to some data on crack growth and corresponding goodness-of-fit tests are carried out. Prediction calculations for failure times defined in terms of degradation level passages are developed and illustrated. 相似文献
20.
Tieling Zhang 《统计学通讯:模拟与计算》2013,42(3):579-592
A consecutive k-out-of-n: G system consists of n linearly ordered components functions if and only if at least k consecutive components function. In this article we investigate the consecutive k-out-of-n: G system in a setup of multicomponent stress-strength model. Under this setup, a system consists of n components functions if and only if there are at least k consecutive components survive a common random stress. We consider reliability and its estimation of such a system whenever there is a change and no change in strength. We provide minimum variance unbiased estimation of system reliability when the stress and strength distributions are exponential with unknown scale parameters. A nonparametric minimum variance unbiased estimator is also provided. 相似文献