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|Title: ||Extensions on long-term survivor model with random effects|
|Other Titles: ||Dui sui ji xiao ying hun he zhi yu mo xing de yi xie tui guang|
|Authors: ||Lai, Xin (賴欣)|
|Department: ||Department of Management Sciences|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2009|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Survival analysis (Biometry)|
Clinical trials -- Statistical methods.
|Notes: ||CityU Call Number: R853.S7 L34 2009|
x, 126 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2009.
Includes bibliographical references (leaves 118-126)
|Abstract: ||Cured patients (or the so called long-term survivors) are increasingly being observed
in clinical trial studies. As exemplified in some data sets, a considerable portion of the
patients are deemed to be cured. With the presence of random hospital/centre effects,
long-term survivor model has been proposed to analyze clustered survival data with a
possible portion of cured patients. Under such mixture modeling setting, several
extensions on random effects cure model are investigated in this thesis to
accommodate the dependence among outcomes which often originate from the
multi-centre research design settings.
Firstly, by taking the possible dependence of random effects into account, the
long-term survivor model with bivariate random effects is proposed to assess the
covariates and random effects in both recovery probability and the instantaneous
failure rate. This study extends earlier work by allowing the random effects in the cure
fraction and hazard function part to follow the bivariate normal distribution, which
gives a generalized model with an additional correlation parameter governing the
relationship between the cure probability and the hazard rate due to the hospital/clinic
Secondly, a hierarchical cure model is considered for survival data obtained from
multilevel research design where the nested random effects are used to model the
hierarchical structure for such kind of survival data. In the modeling framework, multilevel random effect terms are incorporated into the Cox’s proportional hazards
function and the cured probability via a logistic transform, for handling the
hierarchical clustering effects presented in the observed data. The proposed model is
originally developed for multilevel clustered survival data. With some modifications,
it is also applicable to multilevel recurrent failure time data.
Thirdly, through the Box-Cox transformation, a generalized long-term survivor model
is proposed to allow flexibility in specifying the hazard function. With the general
relative risk function form, the failure rate of those at-risk patients is no longer
constraint to the Cox’s proportional hazard function. In particular, a family of hazard
function forms are allowed, which takes exponential and linear relative risk as two
special cases. The parameter governing the power transformation could be determined
by means of a modified Akaike information criterion (AIC).
Adopting the GLMM method and EM algorithm, the estimation of regression
parameters can be achieved by maximizing a BLUP-type log-likelihood function at
the initial step, and then used to find the REML estimation for the variance
component parameters. Application to some data sets, including the carcinoma data,
bone marrow transplantation data, chronic granulomatous disease data and child
survival study data, illustrates the usefulness of the proposed models.
Furthermore, simulation studies are conducted for each model to evaluate the performance of the estimators, under the proposed numerical estimation scheme. In
general, unbiased estimates for both regression and variance component parameters
are observed and the estimation of standard error is also broadly satisfactory, implying
that the proposed estimation methods perform reasonably well. Some further
discussions and remarks on these proposed models and suggestions on future research
studies are provided.
Keywords: Cured patients; EM algorithm; GLMM; Long-term survivor; Random
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3008233|
|Appears in Collections:||MS - Doctor of Philosophy |
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