  In this paper, we propose a semiparametric, tree based joint latent class
modeling approach (JLCT) to model the joint behavior of longitudinal and
time-to-event data. Existing joint latent class modeling approaches are
parametric and can suffer from high computational cost. The most common
parametric approach, the joint latent class model (JLCM), further restricts
analysis to using time-invariant covariates in modeling survival risks and
latent class memberships. Instead, the proposed JLCT is fast to fit, and can
use time-varying covariates in all of its modeling components. We demonstrate
the prognostic value of using time-varying covariates, and therefore the
advantage of JLCT over JLCM on simulated data. We further apply JLCT to the
PAQUID data set and confirm its superior prediction performance and
orders-of-magnitude speedup over JLCM.
