Nonparametric predictive inference for combined competing risks data

被引：2
|
作者：
Coolen-Maturi, Tahani [1 ]
Coolen, Frank P. A. [2 ]
机构：
[1] Univ Durham, Sch Business, Durham DH1 3LB, England
[2] Univ Durham, Dept Math Sci, Durham DH1 3LE, England
来源：
RELIABILITY ENGINEERING & SYSTEM SAFETY | 2014年 / 126卷
关键词：
Imprecise probability; Lower and upper probability; Nonparametric predictive inference; Competing risks; Right-censored data; Combined data; RELIABILITY; MODEL;
D O I：
10.1016/j.ress.2014.01.007
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码：87 / 97
页数：11
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