Article ID Journal Published Year Pages File Type
415297 Computational Statistics & Data Analysis 2008 9 Pages PDF
Abstract

Many generalizations of the Cox proportional hazard method have been elaborated to analyse recurrent event data. The Andersen–Gill model was proposed to handle event data following Poisson processes. This method is compared with non-survival approaches, such as Poisson and negative binomial regression. The comparison is performed on data simulated according to various event-generating processes and differing in subject heterogeneity. When robust standard error estimates are applied, for Poisson processes the Andersen–Gill approach is comparable to a negative binomial regression, whereas the poisson regression has comparable coverage probabilities of confidence intervals, but increased type I error rates; however, none of the methods can generate unbiased parameter estimates with data violating the independent increment assumption. These findings are illustrated by data from a clinical trial of the efficacy of a new pneumococcal vaccine for prevention of otitis media.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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