期刊
LIFETIME DATA ANALYSIS
卷 11, 期 2, 页码 151-174出版社
SPRINGER
DOI: 10.1007/s10985-004-0381-0
关键词
cox proportional hazard model; EM algorithm; maximum likelihood estimator; mixed model; profile likelihood
资金
- NHLBI NIH HHS [R01 HL69720] Funding Source: Medline
In biomedical studies, interest often focuses on the relationship between patient's characteristics or some risk factors and both quality of life and survival time of subjects under study. In this paper, we propose a simultaneous modelling of both quality of life and survival time using the observed covariates. Moreover, random effects are introduced into the simultaneous models to account for dependence between quality of life and survival time due to unobserved factors. EM algorithms are used to derive the point estimates for the parameters in the proposed model and pro. le likelihood function is used to estimate their variances. The asymptotic properties are established for our proposed estimators. Finally, simulation studies are conducted to examine the finite-sample properties of the proposed estimators and a liver transplantation data set is analyzed to illustrate our approaches.
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