Assessing gamma frailty models for clustered failure time data

1995 ◽  
Vol 1 (2) ◽  
pp. 205-220 ◽  
Author(s):  
Joanna H. Shih ◽  
Thomas A. Louis
Keyword(s):  
Failure Time ◽  
Frailty Models ◽  
Failure Time Data ◽  
Time Data ◽  
Gamma Frailty ◽  
Clustered Failure Time Data

scholarly journals Semiparametric Frailty Models for Clustered Failure Time Data

Biometrics ◽  
2011 ◽  
Vol 68 (2) ◽  
pp. 429-436 ◽  
Author(s):  
Zhangsheng Yu ◽  
Xihong Lin ◽  
Wanzhu Tu
Keyword(s):  
Failure Time ◽  
Frailty Models ◽  
Failure Time Data ◽  
Time Data ◽  
Clustered Failure Time Data

A Class of Bayesian Shared Gamma Frailty Models with Multivariate Failure Time Data

Biometrics ◽  
2005 ◽  
Vol 61 (1) ◽  
pp. 208-216 ◽  
Author(s):  
Guosheng Yin ◽  
Joseph G. Ibrahim
Keyword(s):  
Failure Time ◽  
Frailty Models ◽  
Failure Time Data ◽  
Time Data ◽  
Multivariate Failure Time ◽  
Gamma Frailty

The power of testing a semi-parametric shared gamma frailty parameter in failure time data

2008 ◽  
Vol 27 (21) ◽  
pp. 4328-4339
Author(s):  
Mehdi Rahgozar ◽  
Soghrat Faghihzadeh ◽  
Gholamreza Babaee Rouchi ◽  
Yingwei Peng
Keyword(s):  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Gamma Frailty

scholarly journals Regression analysis of clustered interval-censored failure time data

2012 ◽  
Author(s):  
◽  
Junlong Li
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Marginal Model ◽  
Failure Time Data ◽  
Time Data ◽  
Regression Parameters ◽  
Interval Censored ◽  
Clustered Failure Time Data

Clustered failure time data occur when the failure times of interest are clustered into small groups, while interval censoring occurs when the event of interest cannot be observed directly and is only known to have occurred over a time interval. Clustered failure time data often arise together with interval-censoring, which leads to the clustered interval-censored failure time data. In this dissertation, we will focus on the regression analysis of such data. In the first part of the dissertation, a regression analysis under a Cox frailty model is discussed by employing a sieve estimation procedure. In particular, a two-step algorithm is developed for the regression parameter estimation and the asymptotic properties of the resulting sieve maximum likelihood estimates are established. The second part of this dissertation proposes an estimating equation-based approach for the additive hazards model. A major advantage of the proposed method is that it does not involve estimation of any baseline hazard function. Both asymptotic and finite sample properties of the proposed estimates of regression parameters are established and the method is illustrated by the data arising from a lymphatic filariasis study. The last part of the dissertation considers the regression analysis of the same type of data in the context of the linear transformation models. For the inference about the regression parameters, a marginal model approach based on within-cluster resampling (WCR) method is proposed and its large sample properties are also established.

scholarly journals Additive Mixed Effect Model for Clustered Failure Time Data

Biometrics ◽  
2011 ◽  
Vol 67 (4) ◽  
pp. 1340-1351 ◽  
Author(s):  
Jianwen Cai ◽  
Donglin Zeng
Keyword(s):  
Failure Time ◽  
Failure Time Data ◽  
Mixed Effect Model ◽  
Time Data ◽  
Mixed Effect ◽  
Effect Model ◽  
Clustered Failure Time Data
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