title: A Smooth Bootstrap Procedure towards Deriving Confidence Intervals for the Relative Risk.
abstract: Given a pair of sample estimators of two independent proportions, bootstrap methods are a common strategy towards deriving the associated confidence interval for the relative risk. We develop a new smooth bootstrap procedure, which generates pseudo-samples from a continuous quantile function. Under a variety of settings, our simulation studies show that our method possesses a better or equal performance in comparison with asymptotic theory based and existing bootstrap methods, particularly for heavily unbalanced data in terms of coverage probability and power. We illustrate our procedure as applied to several published data sets.
authors.affiliation: Department of Public Health and Preventive Medicine, State University of New York Upstate Medical University, Syracuse, New York, USA.
authors.forename: Dongliang
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.
authors.forename: Alan D
authors.initials: AD
authors.lastname: Hutson
keywords: Bootstrap; Confidence interval; Quantile function; Relative risk