Bootstrap Based Confidence Interval Estimation of \\[6pt]Quantiles for Current Status Data

Wei Chen,Fengling Ren

doi:10.5539/ijsp.v10n5p38

Abstract

In this paper, we proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters. The reasonability of the proposed method is verified by the well performance presented in the extensive simulation study. We also analyzed a real data set as illustration.

Highlights

Current status data, called the ”case 1” interval censored data, arise extensively in epidemiological studies, clinical trials, and other areas, where the time of occurrence of some event is of interest, but one only know whether the event has occurred or not at the examination time
We proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters
Called the ”case 1” interval censored data, arise extensively in epidemiological studies, clinical trials, and other areas, where the time of occurrence of some event is of interest, but one only know whether the event has occurred or not at the examination time

Summary

Introduction

Called the ”case 1” interval censored data, arise extensively in epidemiological studies, clinical trials, and other areas, where the time of occurrence of some event is of interest, but one only know whether the event has occurred or not at the examination time. As noted by Groeneboom and Wellner (2005), the difficulty in computation of κ is enormous, and the resulting performance is rather unstable To avoid this trouble, they suggested the likelihood ratio approach and conducted a small simulation study. Sen and Xu (2015) developed a model based bootstrap method for mixed case interval censored data, which is consistent and can be seen as a ”bootstrap residual” approach combined with the smooth estimator of F, and their simulation study showed their procedure has superior performance. We are interested to construct the confident interval of quantiles for current status data without covariates. The current status model based bootstrap method is presented, where a consistent estimator of standard error of NPMLE of a quantile is proposed.

Methods

Numerical Studies

Application

Discussions