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51.
Multivariate failure time data are commonly encountered in biomedical research since each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. In this article, we use the frailty approach to catch the related survival variables and assume each event is a discrete analog as an interval of clinical examinations periodically. For estimation, an Expectation–Maximization (EM) algorithm is developed and is applied to the diabetic retinopathy study (DRS). 相似文献
52.
Jaap H. Abbring Gerard J. van den Berg 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(3):701-710
Summary. We prove identification of dependent competing risks models in which each risk has a mixed proportional hazard specification with regressors, and the risks are dependent by way of the unobserved heterogeneity, or frailty, components. We show that the conditions for identification given by Heckman and Honoré can be relaxed. We extend the results to the case in which multiple spells are observed for each subject. 相似文献
53.
P. Economou 《统计学通讯:模拟与计算》2013,42(6):1295-1307
We define a parametric proportional odds frailty model to describe lifetime data incorporating heterogeneity between individuals. An unobserved individual random effect, called frailty, acts multiplicatively on the odds of failure by time t. We investigate fitting by maximum likelihood and by least squares. For the latter, the parametric survivor function is fitted to the nonparametric Kaplan–Meier estimate at the observed failure times. Bootstrap standard errors and confidence intervals are obtained for the least squares estimates. The models are applied successfully to simulated data and to two real data sets. Least squares estimates appear to have smaller bias than maximum likelihood. 相似文献
54.
Samuel Soubeyrand Joël Chaduf Ivan Sache Christian Lannou 《Statistical Methodology》2006,3(4):464-482
We propose a method for specifying the distribution of random effects included in a model for cluster data. The class of models we consider includes mixed models and frailty models whose random effects and explanatory variables are constant within clusters. The method is based on cluster residuals obtained by assuming that the random effects are equal between clusters. We exhibit an asymptotic relationship between the cluster residuals and variations of the random effects as the number of observations increases and the variance of the random effects decreases. The asymptotic relationship is used to specify the random-effects distribution. The method is applied to a frailty model and a model used to describe the spread of plant diseases. 相似文献
55.
R. Henderson & P. Oman 《Journal of the Royal Statistical Society. Series B, Statistical methodology》1999,61(2):367-379
Unexplained heterogeneity in univariate survival data and association in multivariate survival can both be modelled by the inclusion of frailty effects. This paper investigates the consequences of ignoring frailty in analysis, fitting misspecified Cox proportional hazards models to the marginal distributions. Regression coefficients are biased towards 0 by an amount which depends in magnitude on the variability of the frailty terms and the form of frailty distribution. The bias is reduced when censoring is present. Fitted marginal survival curves can also differ substantially from the true marginals. 相似文献
56.
The proportional hazards mixed-effects model (PHMM) was proposed to handle dependent survival data. Motivated by its application
in genetic epidemiology, we study the interpretation of its parameter estimates under violations of the proportional hazards
assumption. The estimated fixed effect turns out to be an averaged regression effect over time, while the estimated variance
component could be unaffected, inflated or attenuated depending on whether the random effect is on the baseline hazard, and
whether the non-proportional regression effect decreases or increases over time. Using the conditional distribution of the
covariates we define the standardized covariate residuals, which can be used to check the proportional hazards assumption.
The model checking technique is illustrated on a multi-center lung cancer trial. 相似文献