Competing risks with frailty models when treatment affects only one failure type

Biometrika ◽  
1996 ◽  
Vol 83 (2) ◽  
pp. 467-471 ◽  
Author(s):  
D Faraggi
Biometrika ◽  
1992 ◽  
Vol 79 (4) ◽  
pp. 855-857 ◽  
Author(s):  
ANUP DEWANJI
Keyword(s):  

2014 ◽  
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pp. 4590-4604 ◽  
Author(s):  
Il Do Ha ◽  
Minjung Lee ◽  
Seungyoung Oh ◽  
Jong-Hyeon Jeong ◽  
Richard Sylvester ◽  
...  

Biometrika ◽  
1990 ◽  
Vol 77 (1) ◽  
pp. 207-211 ◽  
Author(s):  
E. GOETGHEBEUR ◽  
L. RYAN

2013 ◽  
Vol 20 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Yuanye Zhang ◽  
Ming-Hui Chen ◽  
Joseph G. Ibrahim ◽  
Donglin Zeng ◽  
Qingxia Chen ◽  
...  

2014 ◽  
Vol 42 (2) ◽  
pp. 427-437 ◽  
Author(s):  
Antai Wang ◽  
Krishnendu Chandra ◽  
Ruihua Xu ◽  
Junfeng Sun

Author(s):  
DANIEL Y.T. FONG ◽  
PAUL YIP

This paper is concerned with testing for the equality of failure rates in a competing risks model with two risks. Three testing procedures are investigated, namely Score test, Likelihood Ratio test and Wald test. Wald test has been considered to be the most powerful in multivariate linear regression analysis.1 However in our application, Wald test is the most efficient one when the failure rate of the second failure type is strictly smaller than the first failure type, otherwise the Score or the Likelihood Ratio test is preferred. This phenomenon is illustrated by data from a mechanical-switch life test.2 The results has been extended to a k-competing risks model. A simulation study is also given to examine the performance of the three tests.


Author(s):  
M. M. E. Abd El-Monsef ◽  
M. M. El-Awady

New classes of continuous distributions have been generated, in the last decad, based on a compounding procedure arises on a latent competing risks problem. This procedure assumes the homogeneity between the population individuals. In this paper, a new lifetime distribution is generated, assuming the heterogeneity at both population and individual levels, called Extended Gamma Gompertz (EGG) distribution. This distribution shows very desirable exibility of its hazard function. Some properties of the proposed distribution are given. Maximum likelihood estimation technique is used to estimate the parameters. A simulation study is performed to examine the performance of the proposed model. Finally, application to a real data set is given to exemplify the utility of the EGG distribution.


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