Joint Modelling of Longitudinal Quality of Life Measurements and Survival Data in Cancer Clinical Trials
MetadataShow full item record
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.