Semiparametric analysis of recurrent events: artificial censoring, truncation, pairwise estimation and inference.
The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to data from an HIV clinical trial as with a limited simulation study.
Ghosh
Debashis
D
Department of Statistics and Public Health Sciences, Penn State University, 514A Wartik Lab, University Park, PA 16802, USA. ghoshd@psu.edu
D017088:AIDS-Related Opportunistic Infections / Q000453:epidemiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016049:Didanosine / Q000627:therapeutic use; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D053208:Kaplan-Meier Estimate; D015233:Models, Statistical; D012008:Recurrence; D016047:Zalcitabine / Q000627:therapeutic use
D016047:Zalcitabine; D016049:Didanosine