Asymptotics for a Class of Dynamic Recurrent Event Models

TL;DR

This paper establishes the asymptotic properties of estimators in a broad class of dynamic recurrent event models, accounting for interventions, covariates, and dependent censoring, with applications in biostatistics and reliability.

Contribution

It introduces a general framework for dynamic recurrent event models and proves their estimators' consistency and weak convergence, enabling advanced statistical inference.

Findings

Proved asymptotic consistency of estimators.

Established weak convergence results.

Applicable to models with interventions and covariates.

Abstract

Asymptotic properties, both consistency and weak convergence, of estimators arising in a general class of dynamic recurrent event models are presented. The class of models take into account the impact of interventions after each event occurrence, the impact of accumulating event occurrences, the induced informative and dependent right-censoring mechanism due to the data-accrual scheme, and the effect of covariate processes on the recurrent event occurrences. The class of models subsumes as special cases many of the recurrent event models that have been considered in biostatistics, reliability, and in the social sciences. The asymptotic properties presented have the potential of being useful in developing goodness-of-fit and model validation procedures, confidence intervals and confidence bands constructions, and hypothesis testing procedures for the finite- and infinite-dimensional parameters of a general class of dynamic recurrent event models, albeit the models without frailties.