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Abstract:
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Interval censoring means that an event time is in an interval (L,R], with L the last examination time before the event, and R the first after. In the univariate case, parametric models are easily fitted, and for non-parametric models, the mass is placed on some intervals, derived from the L and R points. Asymptotic results are simple for the former and complicated for the latter. Parametric models extend easily to multivariate data, like eruption times for teeth, examined at visits to the dentist. However, nonparametric models are intrinsically more complicated. It is difficult to derive the intervals with positive mass and estimated interval probabilities may not be unique. A semiparametric model makes a compromise, with a parametric model, like a frailty model, for the dependence and a nonparametric model for the marginal. I will compare and discuss these three models.
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