A non-parametric estimator for excess recurrent events
Abstract
Measuring disease burden is an important part of both public health research and health economics. Some measures of disease burden, such as hospitalisations, are made up of recurrent events. Assessing what events are related to a particular disease is however non-trivial. We extend the notion of relative survival to recurrent events by developing a novel non-parametric estimator. The estimator combines data from some cohort with aggregated population level data to estimate the number of excess recurrent events. Using empirical process theory, we show that the estimator converges weakly to a mean zero Gaussian process under mild regularity conditions, and provide a consistent estimator for the covariance function. We also evaluate the finite sample properties of the estimator through simulations and provide a practical example using data from Swedish patients with rectal cancer.
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BibTeX
@misc{hedberg2026nonparametric,
title = {{A non-parametric estimator for excess recurrent events}},
author = {Jonatan Hedberg and Ola Hössjer and Caroline Nordenwall and Therese M-L Andersson and Elisavet Syriopoulou},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.26328},
eprint = {2609.26328},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.26328},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A non-parametric estimator for excess recurrent events
AU - Jonatan Hedberg
AU - Ola Hössjer
AU - Caroline Nordenwall
AU - Therese M-L Andersson
AU - Elisavet Syriopoulou
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.26328
UR - https://arxiv.org/abs/2609.26328
AB - Measuring disease burden is an important part of both public health research and health economics. Some measures of disease burden, such as hospitalisations, are made up of recurrent events. Assessing what events are related to a particular disease is however non-trivial. We extend the notion of relative survival to recurrent events by developing a novel non-parametric estimator. The estimator combines data from some cohort with aggregated population level data to estimate the number of excess recurrent events. Using empirical process theory, we show that the estimator converges weakly to a mean zero Gaussian process under mild regularity conditions, and provide a consistent estimator for the covariance function. We also evaluate the finite sample properties of the estimator through simulations and provide a practical example using data from Swedish patients with rectal cancer.
ER -