Nonparametric Estimation of Transition Intensities in Interval-Censored Markov Multistate Models Without Loops

Interval-censored multistate data is collected when the state of a subject is observed periodically. The analysis of such data using nonparametric multistate models was not possible until recently but is very desirable as it allows for more flexibility than its parametric counterparts. The single available result to date has some unique drawbacks. We propose a nonparametric estimator of the transition intensities for interval-censored multistate data using an Expectation Maximization algorithm. The method allows for a mix of interval-censored and right-censored (exactly observed) transitions. A condition to check for the convergence of the algorithm is given. A simulation study comparing the proposed estimator to a consistent estimator is performed and shown to yield near identical estimates at smaller computational cost. A data set on the emergence of teeth in children is analyzed. Software to perform the analyses is publicly available.