Speaker
Description
In many medical applications of event-history analysis, individuals may experience several intermediate events before death, and a non-negligible proportion of deaths is unrelated to the disease under study. While standard multi-state models evaluate the occurrence of different events over time, they do not explicitly model mortality from disease-related causes and from other (population) causes as separate outcomes. To address this, we developed an extended multi-state model based on relative survival [1], which decomposes total (observed) mortality into disease-related (excess) and non-disease-related (population) components with and without intermediate events, in the case when cause of death is unavailable or uncertain.
Within this framework, we can define transition hazards and transition probabilities and derive non-parametric estimators that incorporate population mortality tables into the estimation process. These estimators enable the estimation of hazards and probabilities of population and excess death with and without intermediate events, and their associated uncertainty.
For incorporating covariates and individual prediction, we develop regression models for the excess hazards in the multi-state setting, applying both the multiplicative Cox-type and the additive Aalen modelling frameworks. Two main challenges arise in this context: handling delayed entry (relevant for intermediate states) and addressing small excess death rates (common in later follow-up periods). Both challenges can also appear in simpler settings (where only overall survival is considered or there are no intermediate events). We demonstrate how both challenges are handled within the two modelling frameworks and use simulations to investigate their performance under various scenarios.
As the total mortality in the multi-state model is split into population and excess components, questions arise on the covariate effects and long-term patient predictions that can be obtained from such a model. The two regression approaches address these questions and provide a further understanding of the studied disease. All methods are implemented in the R packages mstate and relsurv, ensuring practical usability.
[1] Manevski D, Putter H, …, de Wreede LC. Integrating relative survival in multi-state models-a non-parametric approach. Stat Methods Med Res. 2022;31(6):997-1012.
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