In cancer clinical trials, longitudinal Quality of Life (QoL)
measurements on a patient may be analyzed by classical linear
mixed models but some patients may drop out of study due to
recurrence or death, which causes problems in the application of
classical methods. Joint modelling of longitudinal QoL
measurements and survival times may be employed to explain the
dropout information of longitudinal QoL measurements, and provide
more efficient estimation, especially when there is strong
association between longitudinal measurements and survival times.
Most joint models in the literature assumed classical linear mixed
model for longitudinal measurements, and Cox's proportional
hazards model for survival times. The linear mixed model with
normal-distribution random effects may not be sufficient to model
longitudinal QoL measurements. Moreover, with advances in medical
research, long-term survivors may exist, which makes the
proportional hazards assumption not suitable for survival times
when some censoring times are due to potential cured patients.
In this thesis, we propose new models to analyze longitudinal QoL
measurements and survival times jointly. In the first part of this
thesis, we develop a joint model which assumes a linear mixed tt
model for longitudinal measurements and a promotion time cure
model for survival data. We link these two models through a latent
variable and develop a semiparametric inference procedure. The
second part of this thesis considers a special feature of the QoL
measurements. That is, they are constrained in an interval
(0,1). We propose to take into account this feature by a
simplex-distribution model for these QoL measurements. Classical
proportional hazards and promotion time cure models are used
separately to the situations, depending on whether a cure fraction
is assumed in the data or not. In both cases, we characterize the
correlation between the longitudinal measurements and survival
times by a shared random effect, and derive a semiparametric
penalized joint partial likelihood to estimate the parameters. The
above proposed new joint models and estimation procedures are
evaluated in simulation studies and applied to the QoL
measurements and recurrence times from a clinical trial on women
with early breast cancer. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2013-01-23 14:04:14.297
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/7759 |
| Date | 23 January 2013 |
| Creators | Song, Hui |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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