01332nas a2200265   4500000000100000008004100001260001600042653001500058653002500073653000900098653002400107653002500131653001100156653002300167653001200190653002500202653004200227100000900269700001100278245006400289300001100353490000700364520068100371022001401052       2008                            d  c2008 Mar 3010aAlgorithms10aAnalysis of Variance10aBias10aComputer Simulation10aConfidence Intervals10aHumans10aLeprostatic Agents10aleprosy10aLongitudinal studies10aRandomized Controlled Trials as Topic1 aLi Y1 aWang Y00aSmooth bootstrap methods for analysis of longitudinal data.  a937-530 v273 a<p>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.</p>  a0277-6715