Monitoring time to event in registry data using CUSUMs based on relative survival models

An aspect of interest in surveillance of diseases is whether the survival time distribution changes over time. By following data in health registries over time, this can be monitored, either in real time or retrospectively. With relevant risk factors registered, these can be taken into account in the monitoring as well. A challenge in monitoring survival times based on registry data is that the information related to cause of death might either be missing or uncertain. To quantify the burden of disease in such cases, relative survival methods can be used, where the total hazard is modelled as the population hazard plus the excess hazard due to the disease.We propose a cumulative sum (CUSUM) procedure for monitoring for changes in the survival time distribution in cases where the use of excess hazard models is relevant. The CUSUM chart is based on a survival log-likelihood ratio and extends previously suggested methods for monitoring of time to event data to the excess hazard setting. The procedure takes into account changes in the population risk over time, as well as changes in the excess hazard which is explained by observed covariates. Properties, challenges and an application to cancer registry data will be presented.

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Publication Details

Journal
Statistical Methods in Medical Research
Published
2026-02-05
DOI
https://doi.org/10.1177/09622802251411540
Primary Topic
Statistical Methods and Inference
Type
article
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article

Monitoring time to event in registry data using CUSUMs based on relative survival models

Jan Terje Kvaløy, Jimmy Huy Tran, Hartwig Kørner
Statistical Methods in Medical Research
Statistical Methods and Inference
article

Monitoring time to event in registry data using CUSUMs based on relative survival models

Jan Terje Kvaløy, Jimmy Huy Tran, Hartwig Kørner
article en

Abstract

An aspect of interest in surveillance of diseases is whether the survival time distribution changes over time. By following data in health registries over time, this can be monitored, either in real time or retrospectively. With relevant risk factors registered, these can be taken into account in the monitoring as well. A challenge in monitoring survival times based on registry data is that the information related to cause of death might either be missing or uncertain. To quantify the burden of disease in such cases, relative survival methods can be used, where the total hazard is modelled as the population hazard plus the excess hazard due to the disease.We propose a cumulative sum (CUSUM) procedure for monitoring for changes in the survival time distribution in cases where the use of excess hazard models is relevant. The CUSUM chart is based on a survival log-likelihood ratio and extends previously suggested methods for monitoring of time to event data to the excess hazard setting. The procedure takes into account changes in the population risk over time, as well as changes in the excess hazard which is explained by observed covariates. Properties, challenges and an application to cancer registry data will be presented.

Statistical Methods in Medical Research
Stavanger University Hospital (NO), University of Bergen (NO), University of Stavanger (NO)
Good health and well-being
Openalex Percentile: Top 68%
Statistical Methods and Inference
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