  We introduce a new framework for creating point-wise confidence intervals for
the distribution of event times for current status data. Existing methods are
based on asymptotics. Our framework is based on binomial properties and
motivates confidence intervals that are very simple to apply and are valid,
i.e., guarantee nominal coverage. Although these confidence intervals are
necessarily conservative for small sample sizes, asymptotically their coverage
rate approaches the nominal one. This binomial framework also motivates
approximately valid confidence intervals, and simulations show that these
approximate intervals generally have coverage rates closer to the nominal level
with shorter length than existing intervals, including the likelihood
ratio-based confidence interval. Unlike previous asymptotic methods that
require different asymptotic distributions for continuous or grid-based
assessment, the binomial framework can be applied to either type of assessment
distribution.
