  In population-based cancer survival analysis, the net survival is important
for government to assess health care programs. For decades, it is observed that
the net survival reaches a plateau after long-term follow-up, this is so called
``statistical cure''. Several methods were proposed to address the statistical
cure. Besides, the cure time can be used to evaluate the time period of a
health care program for a specific patient population, and it also can be
helpful for a clinician to explain the prognosis for patients, therefore the
cure time is an important health care index. However, those proposed methods
assume the cure time to be infinity, thus it is inconvenient to make inference
on the cure time. In this dissertation, we define a more general concept of
statistical cure via conditional survival. Based on the newly defined
statistical cure, the cure time is well defined. We develop cure time model
methodologies and show a variety of properties through simulation. In data
analysis, cure times are estimated for 22 major cancers in Taiwan, we further
use colorectal cancer data as an example to conduct statistical inference via
cure time model with covariate sex, age group, and stage. This dissertation
provides a methodology to obtain cure time estimate, which can contribute to
public health policy making.
