Failure time data analysis, or survival analysis, is involved in various research
fields, such as medicine and public health. One basic assumption in
standard survival analysis is that every individual in the study population
will eventually experience the event of interest. However, this assumption is
usually violated in practice, for example when the variable of interest is the
time to relapse of a curable disease resulting in the existence of long-term survivors.
Also, presence of unobservable risk factors in the group of susceptible
individuals may introduce heterogeneity to the population, which is not properly
addressed in standard survival models. Moreover, the individuals in the
population may be grouped in clusters, where there are associations among observations
from a cluster. There are methodologies in the literature to address
each of these problems, but there is yet no natural and satisfactory way to
accommodate the coexistence of a non-susceptible group and the heterogeneity
in the susceptible group under a univariate setting. Also, various kinds of
associations among survival data with a cure are not properly accommodated.
To address the above-mentioned problems, a class of models is introduced to
model univariate and multivariate data with long-term survivors.
A semiparametric cure model for univariate failure time data with long-term
survivors is introduced. It accommodates a proportion of non-susceptible
individuals and the heterogeneity in the susceptible group using a compound-
Poisson distributed random effect term, which is commonly called a frailty. It
is a frailty-Cox model which does not place any parametric assumption on the
baseline hazard function. An estimation method using multiple imputation
is proposed for right-censored data, and the method is naturally extended to
accommodate interval-censored data. The univariate cure model is extended
to a multivariate setting by introducing correlations among the compound-
Poisson frailties for individuals from the same cluster. This multivariate cure
model is similar to a shared frailty model where the degree of association among
each pair of observations in a cluster is the same. The model is further extended
to accommodate repeated measurements from a single individual leading to
serially correlated observations. Similar estimation methods using multiple
imputation are developed for the multivariate models. The univariate model
is applied to a breast cancer data and the multivariate models are applied
to the hypobaric decompression sickness data from National Aeronautics and
Space Administration, although the methodologies are applicable to a wide
range of data sets. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/167223 |
| Date | January 2012 |
| Creators | Wong, Kin-yau., 黃堅祐. |
| Contributors | Lam, KF |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B48199473 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
Page generated in 0.0019 seconds