title: Second-order analysis of semiparametric recurrent event processes.
abstract: A typical recurrent event dataset consists of an often large number of recurrent event processes, each of which contains multiple event times observed from an individual during a follow-up period. Such data have become increasingly available in medical and epidemiological studies. In this article, we introduce novel procedures to conduct second-order analysis for a flexible class of semiparametric recurrent event processes. Such an analysis can provide useful information regarding the dependence structure within each recurrent event process. Specifically, we will use the proposed procedures to test whether the individual recurrent event processes are all Poisson processes and to suggest sensible alternative models for them if they are not. We apply these procedures to a well-known recurrent event dataset on chronic granulomatous disease and an epidemiological dataset on meningococcal disease cases in Merseyside, United Kingdom to illustrate their practical value.
authors.affiliation: Division of Biostatistics, Yale University, New Haven, Connecticut 06520, USA. yongtao.guan@yale.edu
authors.forename: Yongtao
authors.initials: Y
authors.lastname: Guan
mesh_terms: D001699:Biometry / Q000379:methods; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D006105:Granulomatous Disease, Chronic / Q000473:pathology; D006801:Humans; D008589:Meningococcal Infections / Q000453:epidemiology; D015233:Models, Statistical; D012008:Recurrence; D006113:United Kingdom