Cure rate models, introduced by Boag (1949), are very commonly used while modelling
lifetime data involving long time survivors. Applications of cure rate models can be seen
in biomedical science, industrial reliability, finance, manufacturing, demography and criminology. In this thesis, cure rate models are discussed under a competing cause scenario,
with the assumption of proportional odds (PO) lifetime distributions for the susceptibles,
and statistical inferential methods are then developed based on right-censored data.
In Chapter 2, a flexible cure rate model is discussed by assuming the number of competing
causes for the event of interest following the Conway-Maxwell (COM) Poisson distribution,
and their corresponding lifetimes of non-cured or susceptible individuals can be
described by PO model. This provides a natural extension of the work of Gu et al. (2011)
who had considered a geometric number of competing causes. Under right censoring, maximum likelihood estimators (MLEs) are obtained by the use of expectation-maximization
(EM) algorithm. An extensive Monte Carlo simulation study is carried out for various scenarios,
and model discrimination between some well-known cure models like geometric,
Poisson and Bernoulli is also examined. The goodness-of-fit and model diagnostics of the
model are also discussed. A cutaneous melanoma dataset example is used to illustrate the
models as well as the inferential methods.
Next, in Chapter 3, the destructive cure rate models, introduced by Rodrigues et al. (2011), are discussed under the PO assumption. Here, the initial number of competing
causes is modelled by a weighted Poisson distribution with special focus on exponentially
weighted Poisson, length-biased Poisson and negative binomial distributions. Then, a damage
distribution is introduced for the number of initial causes which do not get destroyed.
An EM-type algorithm for computing the MLEs is developed. An extensive simulation
study is carried out for various scenarios, and model discrimination between the three
weighted Poisson distributions is also examined. All the models and methods of estimation
are evaluated through a simulation study. A cutaneous melanoma dataset example is used
to illustrate the models as well as the inferential methods.
In Chapter 4, frailty cure rate models are discussed under a gamma frailty wherein the
initial number of competing causes is described by a Conway-Maxwell (COM) Poisson
distribution in which the lifetimes of non-cured individuals can be described by PO model.
The detailed steps of the EM algorithm are then developed for this model and an extensive
simulation study is carried out to evaluate the performance of the proposed model and the
estimation method. A cutaneous melanoma dataset as well as a simulated data are used for
illustrative purposes.
Finally, Chapter 5 outlines the work carried out in the thesis and also suggests some
problems of further research interest. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23906 |
| Date | January 2019 |
| Creators | FENG, TIAN |
| Contributors | Balakrishnan, Narayanaswamy, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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