Current status data with two competing risks and missing failure types: a parametric approach

被引：3

作者：

Koley, Tamalika ^{[1
]}

Dewanji, Anup ^{[1
]}

机构：

[1] Indian Stat Inst, Appl Stat Unit, Kolkata 700108, W Bengal, India

来源：

JOURNAL OF APPLIED STATISTICS | 2022年 / 49卷 / 07期

关键词：

Monitoring time; missing not at random (MNAR); sub-distribution function; masking probabilities; identifiability; maximum-likelihood estimation; INFERENCE; SURVIVAL;

D O I：

10.1080/02664763.2021.1881453

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：

Missing cause of failure is a common problem in competing risks data. Here we consider a general missing pattern in which one observes a set of possible causes containing the true cause. In this work, we focus on the parametric analysis of current status data with two competing risks and the above-mentioned missing pattern. We make some simpler assumptions on the conditional probability of observing a set of possible causes of failure given the true cause and carry out maximum-likelihood estimation of the model parameters. Asymptotic properties of the maximum-likelihood estimators are also discussed. Simulation studies are performed to study the finite sample properties of the estimators and also to investigate the role of the monitoring time distribution. Finally, the method is illustrated through the analysis of a real data set.

引用

页码：1769 / 1783

页数：15

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