Pointwise confidence intervals for a survival distribution with small samples or heavy censoring

We propose a beta product confidence procedure (BPCP) that is a non-parametric confidence procedure for the survival curve at a fixed time for right-censored data assuming independent censoring. In such situations, the Kaplan-Meier estimator is typically used with an asymptotic confidence interval (CI) that can have coverage problems when the number of observed failures is not large, and/or when testing the latter parts of the curve where there are few remaining subjects at risk. The BPCP guarantees central coverage (i.e. ensures that both one-sided error rates are no more than half of the total nominal rate) when there is no censoring (in which case it reduces to the Clopper-Pearson interval) or when there is progressive type II censoring (i.e. when censoring only occurs immediately after failures on fixed proportions of the remaining individuals). For general independent censoring, simulations show that the BPCP maintains central coverage in many situations where competing methods can have very substantial error rate inflation for the lower limit. The BPCP gives asymptotically correct coverage and is asymptotically equivalent to the CI on the Kaplan-Meier estimator using Greenwood's variance. The BPCP may be inverted to create confidence procedures for a quantile of the underlying survival distribution. Because the BPCP is easy to implement, offers protection in settings when other methods fail, and essentially matches other methods when they succeed, it should be the method of choice.