Smooth bootstrap methods for analysis of longitudinal data.

Printer-friendly version
TitleSmooth bootstrap methods for analysis of longitudinal data.
Publication TypeJournal Article
AuthorsLi Y, Wang Y-G
Abbrev. JournalStat Med
JournalStatistics in medicine
Year of Publication2008
Volume27
Issue7
Pagination937-53
Publication Languageeng
KeywordsAlgorithms, Analysis of Variance, Bias, Computer Simulation, Confidence Intervals, Humans, Leprostatic Agents, Leprosy, Longitudinal studies, Randomized Controlled Trials as Topic
Abstract

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.

PubMed URL

http://www.ncbi.nlm.nih.gov/pubmed/17701950?dopt=Abstract

DOI10.1002/sim.3027

More resources on: