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.
Clement
David Y
DY
Department of Statistical Science, Cornell University, Ithaca, NY 14853-7801, USA. dyc24@cornell.edu
Strawderman
Robert L
RL
D000328:Adult; D001249:Asthma / Q000517:prevention & control; D001699:Biometry; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007223:Infant; D008137:Longitudinal Studies; D008597:Menstrual Cycle; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012008:Recurrence; D013997:Time Factors