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- W918430672 abstract "Cured patients (or the so called long-term survivors) are increasingly being observed in clinical trial studies. As exemplified in some data sets, a considerable portion of the patients are deemed to be cured. With the presence of random hospital/centre effects, long-term survivor model has been proposed to analyze clustered survival data with a possible portion of cured patients. Under such mixture modeling setting, several extensions on random effects cure model are investigated in this thesis to accommodate the dependence among outcomes which often originate from the multi-centre research design settings. Firstly, by taking the possible dependence of random effects into account, the long-term survivor model with bivariate random effects is proposed to assess the covariates and random effects in both recovery probability and the instantaneous failure rate. This study extends earlier work by allowing the random effects in the cure fraction and hazard function part to follow the bivariate normal distribution, which gives a generalized model with an additional correlation parameter governing the relationship between the cure probability and the hazard rate due to the hospital/clinic effects. Secondly, a hierarchical cure model is considered for survival data obtained from multilevel research design where the nested random effects are used to model the hierarchical structure for such kind of survival data. In the modeling framework," @default.
- W918430672 created "2016-06-24" @default.
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- W918430672 date "2009-01-01" @default.
- W918430672 modified "2023-09-23" @default.
- W918430672 title "Extensions on long-term survivor model with random effects" @default.
- W918430672 hasPublicationYear "2009" @default.
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