Weighted Accelerated Failure Time Models and Their Applications In Clustered Data




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In survival analysis, semiparametric accelerated failure time (AFT) model postulates a log-linear model for the failure times and covariates with an unspecified error, which is a very useful alternative to proportional hazard model. Clustered failure time data often arise from biomedical research. There are several challenges in modeling the clustered failure time distribution: within-cluster dependency, right censoring, and the unknown relationship between covariates and failure times. In this dissertation, we propose a new estimation method, weighted least-squares approach, for the semiparametric AFT model to estimate the parameters of interest for mixture cure data and case-cohort data, separately. The weighted least-squares approach is not only very intuitive but also can be easily extend to clustered data by incorporating generalized estimating equation (GEE). Currently, there are about 5.6 million people in America are suffering from Alzheimer’s disease (AD). Unfortunately, AD has no current cure. Mouse memory study is carried out to better understand the pathogenesis of AD. Based on the data structure analysis of mouse memory data, we propose weighted least-squares approach to semiparametric AFT mixture cure model to estimate the cured rate of treatment and the failure time distribution at the same time in Chapter 2. It is further extended to clustered data by taking within-cluster dependency into account through GEE. Large scale simulations are conducted to investigate the properties of the proposed estimators. The proposed method is applied to mouse memory data to investigate the effect of specific gene expressions on mouse memory. In the biomedical research, two analysis challenges often arise. The first challenge is that some main covariates of interest are time consuming or very expensive to measure; the second challenge is that the outcome in the data set is rare. In Chapter 3, weighted least-squares approach is proposed to semiparametric AFT model for case-cohort design where inverse probability weights (IPW) is used to correct the sampling bias. It is also extended to clustered case-cohort data where the within-cluster dependency is accounted for by GEE. The performance of the proposed model is evaluated by large scale simulations. An application to a retrospective dental study is conducted.



Survival analysis (Biometry), Least squares, Generalized estimating equations, Failure time data analysis, Biomedical materials -- Research