Joint longitudinal and survival-cure models in tumour xenograft experiments

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival‐cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival‐cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors.