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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Li Y,Wang YG

doi

10.1002/sim.3027

subject

Has Abstract

pub_date

2008-03-30 00:00:00

pages

937-53

issue

7

eissn

0277-6715

issn

1097-0258

journal_volume

27

pub_type

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