Joint modeling of varying-disease-state longitudinal ordinal data and time-to-event data with application to alzheimer’s disease

Joint models are routinely used in clinical trials to fit longitudinal and survival data simulta- neously in an integrated fashion. We propose a joint model that incorporates state-specific trajectories for fitting the longitudinal ordinal response and time-to-event data, focusing on its application to Alzheimer’s Disease (AD). The proposed joint model effectively captures the fluctuating cognitive conditions observed before and after the transition between two disease states. By integrating longitudinal data into the survival sub-model through shared trajectories, we can improve the fit of the survival data. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed to carry out Bayesian computation. A variation of the Deviance Information Criterion is developed to assess the fit of each component of the joint model as well as the contribution of the longitudinal data in fitting the survival data. A variation of the concordance (C) index is further derived to assess the discriminatory and predictive performance of the longitudinal sub-model. An in-depth analysis of the real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database is carried out to demonstrate the applicability of the proposed methodology. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 49-73.