In the analysis of correlated data, it is ideal to capture the true dependence structure to increase effciency of the estimation. However, for multivariate survival data, this is extremely
Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations ...Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.展开更多
Many survival studies record the times to two or more distinct failures oneach subject. The failures may be events of different natures or may be repetitions of the same kindof event. In this article, we consider the ...Many survival studies record the times to two or more distinct failures oneach subject. The failures may be events of different natures or may be repetitions of the same kindof event. In this article, we consider the regression analysis of such multivariate failure timedata under the additive hazards model. Simple weighted estimating functions for the regressionparameters 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 testingand model selection are presented, and the asymptotic properties of these statistics are examined.展开更多
The survival analysis literature has always lagged behind the categorical data literature in developing methods to analyze clustered or multivariate data. While estimators based on
We thank all the discussants for their interesting and stimulating contributions. They have touched various aspects that have not been considered by the original articles.
Case-cohort study designs are widely used to reduce the cost of large cohort studies. When several diseases are of interest, we can use the same subcohort. In this paper, we will study the casecohort design of margina...Case-cohort study designs are widely used to reduce the cost of large cohort studies. When several diseases are of interest, we can use the same subcohort. In this paper, we will study the casecohort design of marginal additive hazards model for multiple outcomes by a more efficient version. Instead of analyzing each disease separately, ignoring the additional exposure measurements collected on subjects with other diseases, we propose a new weighted estimating equation approach to improve the efficiency by utilizing as much information collected as possible. The consistency and asymptotic normality of the resulting estimator are established. Simulation studies are conducted to examine the finite sample performance of the proposed estimator, which confirm the efficiency gains.展开更多
文摘In the analysis of correlated data, it is ideal to capture the true dependence structure to increase effciency of the estimation. However, for multivariate survival data, this is extremely
基金Supported by the National Natural Science Foundation of China (11171263)
文摘Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.
基金Supported by the National Natural Science Foundation of China (No. 10471140)Science Foundation of HUBEI (98j081)Scientific Research Great Project of Education Department of HUBEI (2002Z04001).supported by grants from Research Grants Council of
文摘Many survival studies record the times to two or more distinct failures oneach subject. The failures may be events of different natures or may be repetitions of the same kindof event. In this article, we consider the regression analysis of such multivariate failure timedata under the additive hazards model. Simple weighted estimating functions for the regressionparameters 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 testingand model selection are presented, and the asymptotic properties of these statistics are examined.
文摘The survival analysis literature has always lagged behind the categorical data literature in developing methods to analyze clustered or multivariate data. While estimators based on
文摘We thank all the discussants for their interesting and stimulating contributions. They have touched various aspects that have not been considered by the original articles.
基金supported by Graduate Innovation Foundation of Shanghai University of Finance and Economics,China(Grant No.CXJJ2014-453)the second author is supported by National Natural Science Foundation of China(Grant No.11301355)+1 种基金the Technology Foundation for Selected Overseas Chinese Scholar,Ministry of Personnel of BeijingChina
文摘Case-cohort study designs are widely used to reduce the cost of large cohort studies. When several diseases are of interest, we can use the same subcohort. In this paper, we will study the casecohort design of marginal additive hazards model for multiple outcomes by a more efficient version. Instead of analyzing each disease separately, ignoring the additional exposure measurements collected on subjects with other diseases, we propose a new weighted estimating equation approach to improve the efficiency by utilizing as much information collected as possible. The consistency and asymptotic normality of the resulting estimator are established. Simulation studies are conducted to examine the finite sample performance of the proposed estimator, which confirm the efficiency gains.
