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Smooth bootstrap methods for analysis of longitudinal data
Author(s) -
Li Yue,
Wang YouGan
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3027
Subject(s) - estimator , statistics , confidence interval , residual , statistical inference , variance (accounting) , inference , data set , mathematics , computer science , econometrics , algorithm , artificial intelligence , accounting , business
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. Copyright © 2007 John Wiley & Sons, Ltd.