title: Smooth bootstrap methods for analysis of longitudinal data.
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.
authors: Lilly-Singapore Centre for Drug Discovery, 1 Science Park Road #04-01, The Capricorn, Singapore 117528, Singapore.
authors: Yue
authors: Y
authors: Li
authors: You-Gan
authors: YG
authors: Wang
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D015982:Bias; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D007917:Leprostatic Agents / Q000627:therapeutic use; D007918:Leprosy / Q000188:drug therapy; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D007917:Leprostatic Agents