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

TL;DR

This paper introduces a CUSUM-based method for monitoring changes in survival times in registry data, accounting for population risk and covariates using relative survival models, useful in disease surveillance.

Contribution

It extends existing CUSUM methods to the excess hazard setting in relative survival models, enabling more accurate monitoring of survival time changes over time.

Findings

The proposed CUSUM chart effectively detects changes in survival distributions.

Application to cancer registry data demonstrates practical utility.

The method accounts for population risk variations and covariates.

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 CUSUM procedure for monitoring for changes in the survival time distribution in cases where 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.