Marginal regression of gaps between recurrent events.
Recurrent event data typically exhibit the phenomenon of intra-individual correlation, owing to not only observed covariates but also random effects. In many applications, the population may be reasonably postulated as a heterogeneous mixture of individual renewal processes, and the inference of interest is the effect of individual-level covariates. In this article, we suggest and investigate a marginal proportional hazards model for gaps between recurrent events. A connection is established between observed gap times and clustered survival data with informative cluster size. We subsequently construct a novel and general inference procedure for the latter, based on a functional formulation of standard Cox regression. Large-sample theory is established for the proposed estimators. Numerical studies demonstrate that the procedure performs well with practical sample sizes. Application to the well-known bladder tumor data is given as an illustration.
Huang
Yijian
Y
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. eugene@fhcrc.org
Chen
Ying Qing
YQ
D016000:Cluster Analysis; D006801:Humans; D016016:Proportional Hazards Models; D012008:Recurrence; D012044:Regression Analysis; D016019:Survival Analysis; D001749:Urinary Bladder Neoplasms / Q000473:pathology*