  Latent class analysis (LCA) is a useful tool to investigate the heterogeneity
of a disease population with time-to-event data. We propose a new method based
on non-parametric maximum likelihood estimator (NPMLE), which facilitates
theoretically validated inference procedure for covariate effects and
cumulative hazard functions. We assess the proposed method via extensive
simulation studies and demonstrate improved predictive performance over
standard Cox regression model. We further illustrate the practical utility of
the proposed method through an application to a mild cognitive impairment (MCI)
cohort dataset.
