Often in longitudinal studies, some subjects
complete their follow-up visits, but others miss their visits due to various
reasons. For those who miss follow-up visits, some of them might learn that the
event of interest has already happened when they come back. In this case, not
only are their event times interval-censored, but also their time-dependent
measurements are incomplete. This problem was motivated by a national
longitudinal survey of youth data. Maximum likelihood estimation (MLE) method
based on expectation-maximization (EM) algorithm is used for parameter
estimation. Then missing information principle is applied to estimate the
variance-covariance matrix of the MLEs. Simulation studies demonstrate that the
proposed method works well in terms of bias, standard error, and power for
samples of moderate size. The national longitudinal survey of youth 1997
(NLSY97) data is analyzed for illustration.
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