Nonparametric estimation in an "illness-death" model when all transition times are interval censored.

We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states 0,1,2 from the observations with interval censored times of 0 --> 1, 0 --> 2 and 1 --> 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times being interval censored, the times of two events (0 --> 1 and 1 --> 2 transitions) can be censored into the same interval. This development was motivated by a common data structure in oral health research, here specifically illustrated by the data from a prospective cohort study on the longevity of dental veneers. Using the self-consistency algorithm we obtain the maximum likelihood estimators of the cumulative incidences of the times to events 1 and 2 and of the intensity of the 1 --> 2 transition. This work generalizes previous results on the estimation in an "illness-death" model from interval censored observations.