title: A semiparametric bootstrap approach to correlated data analysis problems.
abstract: In this note, we outline a simple to use yet powerful bootstrap algorithm for handling correlated outcome variables in terms of either hypothesis testing or confidence intervals using only the marginal models. This new method can handle combinations of continuous and discrete data and can be used in conjunction with other covariates in a model. The procedure is based upon estimating the family-wise error (FWE) rate and then making a Bonferroni-type correction. A simulation study illustrates the accuracy of the algorithm over a variety of correlation structures.
authors.affiliation: Division of Biostatistics, University at Buffalo, Farber Hall Rm 249A, 3435 Main Street, Buffalo, NY 14214-3000, USA. ahutson@buffalo.edu
authors.forename: Alan D
authors.initials: AD
authors.lastname: Hutson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*