Conditional GEE for recurrent event gap times

This paper deals with the analysis of recurrent event data subject to censored observation.Using a suitable adaptation of generalized estimating equations for longitudinal data, we propose a straightforward methodology for estimating the parameters indexing the conditional means and variances of the process interevent (i.e. gap) times.The proposed methodology permits the use of both time-fixed and time-varying covariates, as well as transformations of the gap times, creating a flexible and useful class of methods for analyzing gap-time data.Censoring is dealt with by imposing a parametric assumption on the censored gap times, and extensive simulation results demonstrate the relative robustness of parameter estimates even when this parametric assumption is incorrect.A suitable large-sample theory is developed.Finally, we use our methods to analyze data from a randomized trial of asthma prevention in young children.