Efficient Assessment of Individualized Disease Risk and Treatment Response via Augmentation
CitationZheng, Yu. 2017. Efficient Assessment of Individualized Disease Risk and Treatment Response via Augmentation. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractT-year survival, defined as the survival status by a pre-specified time point t, is of great interest in many medical research areas. When the t-year survival is the outcome of interest in the individualized medicine, baseline covariates are used to predict the t-year survival for potential treatment response comparison. Time-specific generalized linear models estimated with inverse censoring probability weighting provides more robustness to model misspecification compared to other methods, but some challenges remain in the heavy censoring settings: the prediction model could be quite inefficient and deriving the optimal individualized treatment rules based on maximizing the population average survival probability could be difficult. Chapter 1 presents an imputation-based method to improve the efficiency of the baseline prediction model by incorporating the information from subjects censored before t and auxiliary covariates including the post-baseline secondary outcomes collected before censoring. Chapter 2 extends the method in Chapter 1 to incorporate the post-baseline intermediate covariates that are collected before t but have non-negligible missing values due to censoring. Chapter 3 proposes a systematic approach to derive optimal individualized treatment rules that maximizes the population average survival probability, and imputation-based augmentation approach is also developed to improve the efficiency of the estimation.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41140345
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