The cluster bootstrap consistency in generalized estimating equations

• Published in: Journal of multivariate analysis Vol. 115; pp. 33 - 47
• Main Authors: Cheng, Guang, Yu, Zhuqing, Huang, Jianhua Z.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.03.2013; Taylor & Francis LLC
• Subjects: Approximation; Asymptotic methods; Bootstrap consistency; Bootstrap method; Cluster analysis; Clustered/longitudinal data; Estimating techniques; Exchangeably weighted cluster bootstrap; Generalized estimating equations; One-step bootstrap; Studies; 62F40; Exchangeably weighted cluster bootstrap; One-step bootstrap; 62F12; Clustered/longitudinal data; 62F25; Generalized estimating equations; Bootstrap consistency
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2012.09.003

The cluster bootstrap resamples clusters or subjects instead of individual observations in order to preserve the dependence within each cluster or subject. In this paper, we provide a theoretical justification of using the cluster bootstrap for the inferences of the generalized estimating equations (GEE) for clustered/longitudinal data. Under the general exchangeable bootstrap weights, we show that the cluster bootstrap yields a consistent approximation of the distribution of the regression estimate, and a consistent approximation of the confidence sets. We also show that a computationally more efficient one-step version of the cluster bootstrap provides asymptotically equivalent inference.