Type of Document	Dissertation
Author	Liu, Yang
URN	etd-03242009-145017
Title	Transformation Models for Survival Data Analysis and Applications
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Xu-Feng Niu Committee Chair Dan McGee Committee Member Debajyoti Sinha Committee Member Donald Lloyd Outside Committee Member
Keywords	Cure Rate Models Survival Models
Date of Defense	2009-03-23
Availability	unrestricted
Abstract It is often assumed that all uncensored subjects will eventually experience the event of interest in standard survival models. However, in some situations when the event considered is not death, it will never occur for a proportion of subjects. Survival models with a cure fraction are becoming popular in analyzing this type of study. We propose a generalized transformation model motivated by Zeng et al's (2006) transformed proportional time cure model. In our proposed model, fractional polynomials are used instead of the simple linear combination of the covariates. The proposed models give us more flexibility without loosing any good properties of the original model, such as asymptotic consistency and asymptotic normality of the regression coefficients. The proposed model will better fit the data where the relationship between a response variable and covariates is non-linear. We also provide a power selection procedure based on the likelihood function. A simulation study is carried out to show the accuracy of the proposed power selection procedure. The proposed models are applied to coronary heart disease and cancer related medical data from both observational cohort studies and clinical trials.
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