Variance estimation for clustered recurrent event data with a small number of clusters.
Often in biomedical studies, the event of interest is recurrent and within-subject events cannot usually be assumed independent. In semi-parametric estimation of the proportional rates model, a working independence assumption leads to an estimating equation for the regression parameter vector, with within-subject correlation accounted for through a robust (sandwich) variance estimator; these methods have been extended to the case of clustered subjects. We consider variance estimation in the setting where subjects are clustered and the study consists of a small number of moderate-to-large-sized clusters. We demonstrate through simulation that the robust estimator is quite inaccurate in this setting. We propose a corrected version of the robust variance estimator, as well as jackknife and bootstrap estimators. Simulation studies reveal that the corrected variance is considerably more accurate than the robust estimator, and slightly more accurate than the jackknife and bootstrap variance. The proposed methods are used to compare hospitalization rates between Canada and the U.S. in a multi-centre dialysis study.
Schaubel
Douglas E
DE
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA. deschau@umich.edu
D002170:Canada; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006760:Hospitalization; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D010530:Peritoneal Dialysis; D012008:Recurrence; D051437:Renal Insufficiency / Q000628:therapy; D014481:United States