Transformation Model for Interval Censoring with a Cured Subgroup by Kernel-based Estimation

• Main Authors: Hsin-Yu Yang, 楊新宇
• Other Authors: 陳蔓樺
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/31888676456283846796

碩士 === 淡江大學 === 統計學系碩士班 === 103 === As time progresses, continuous development, there are more and more interval censoring data with clinical trials. Sometimes, it is hard to observe the exact time of event, but we know the observed failure time falls within a time period. In this thesis, we consider mixture cure models for interval censored data with a cured subgroup, where subjects in this subgroup are not susceptible to the event of interest. We suppose logistic regression to estimate cure proportion. In addition, we consider semiparametric transformation models to analysis the event data. We focus on reparametrizing the step function of unknown baseline hazard function by the logarithm of its jump sizes in Chapter 3, and a kernel-based approach for smooth estimation of unknown baseline hazard function in Chapter 4. The EM algorithm is developed for the estimation and simulation studies are conducted.