Asymptotic theory for the Cox model with missing time-dependent covariate

被引:20
|
作者
Dupuy, Jean-Francois
Grama, Ion
Mesbah, Mounir
机构
[1] Univ Toulouse 3, Lab Stat & Probabil, F-31062 Toulouse 9, France
[2] Univ Bretagne Sud, F-56000 Vannes, France
[3] Univ Paris 06, Lab Stat Theoret & Appl, F-75013 Paris, France
来源
ANNALS OF STATISTICS | 2006年 / 34卷 / 02期
关键词
time-dependent Cox model; survival data; missing time-dependent covariate; maximum likelihood estimation; consistency; asymptotic distribution;
D O I
10.1214/009053606000000038
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The relationship between a time-dependent covariate and survival times is usually evaluated via the Cox model. Time-dependent covariates are generally available as longitudinal data collected regularly during the course of the study. A frequent problem, however, is the occurence of missing covariate data. A recent approach to estimation in the Cox model in this case jointly models survival and the longitudinal covariate. However, theoretical justification of this approach is still lacking. In this paper we prove existence and consistency of the maximum likelihood estimators in a joint model. The asymptotic distribution of the estimators is given along with a consistent estimator of the asymptotic variance.
引用
收藏
页码:903 / 924
页数:22
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