Nonparametric maximum likelihood estimation for competing risks survival data subject to interval censoring and truncation

We derive the nonparametric maximum likelihood estimate (NPMLE) of the cumu lative incidence functions for competing risks survival data subject to int erval censoring and truncation. Since the cumulative incidence function NPM LEs give rise to an estimate of the survival distribution which can be unde fined over a potentially larger set of regions than the NPMLE of the surviv al function obtained ignoring failure type, we consider an alternative pseu dolikelihood estimator. The methods are then applied to data from a cohort of injecting drug users in Thailand susceptible to infection from HIV-1 sub types B and E.