Data from transplant patients has many unique characteristics that can cause problems with statistical modeling. The patient’s underlying disease / health trajectory is known to affect both longitudinal biomarker values and the probability of both death and transplant.

Patients’ survival probabilities also change post-transplant, with dependencies on pre-transplant biomarker values. To properly incorporate the clinical features of transplant data, we developed a joint longitudinal-survival model that links an exponential growth-decay longitudinal model to a modified cure survival model. This allows us to evaluate patient biomarker trajectories and survival times both pre- and post-transplant. The models are linked by patient-level shared random effects that appear in the biomarker trajectories and the frailties of the survival functions. Estimates are obtained via the EM algorithm, with random effects integrated out of the complete data likelihood function using adaptive quadrature techniques. This model represents the first steps towards a dynamic prediction model for transplant data.


A/Prof Sarah J Ratcliffe

Research Area

University of Pennsylvania (USA)


Fri, 19/04/2013 - 4:00pm to 5:00pm


OMB-145, Old Main Building, UNSW Kensington Campus