Semiparametric Regression for the Mean and Rate Functions of Recurrent Events

TLDR

The Cox‑type intensity model for recurrent events assumes a time‑transformed Poisson process with multiplicative covariate effects, but recent work has introduced semiparametric procedures that relax this Poisson assumption. This paper rigorously justifies these robust procedures using modern empirical‑process theory. We develop simultaneous confidence bands for the mean function and provide graphical and numerical diagnostics to assess model adequacy. Simulation studies and an application to chronic granulomatous disease infection data demonstrate the advantages of the robust approach.

Abstract

Summary The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate function of the counting process without the Poisson-type assumption. In this paper, we provide a rigorous justification of such robust procedures through modern empirical process theory. Furthermore, we present an approach to constructing simultaneous confidence bands for the mean function and describe a class of graphical and numerical techniques for checking the adequacy of the fitted mean–rate model. The advantages of the robust procedures are demonstrated through simulation studies. An illustration with multiple-infection data taken from a clinical study on chronic granulomatous disease is also provided.