Practicable confidence intervals for current status data

Abstract

Although confidence intervals (CIs) for binary isotonic regression and current status survival data have been well studied theoretically, their practical application has been limited, in part because of poor performance in small samples and in part because of computational difficulties. Ghosh, Banerjee, and Biswas (2008, Biometrics 64, 1009-1017) described three approaches to constructing CIs: (i) the Wald-based method; (ii) the subsampling-based method; and (iii) the likelihood-ratio test (LRT)-based method. In simulation studies, they found that the subsampling-based method and LRT-based method tend to have better coverage probabilities than a simple Wald-based method that may perform poorly in realistic sample sizes. However, software implementing these approaches is currently unavailable. In this article, we show that by using transformations, simple Wald-based CIs can be improved with small and moderate sample sizes to have competitive performance with LRT-based method. Our simulations further show that a simple nonparametric bootstrap gives approximately correct CIs for the data generating mechanisms that we consider. We provide an R package that can be used to compute the Wald-type and the bootstrap CIs and demonstrate its practical utility with two real data analyses.