A multi-state model incorporating estimation of excess hazards and multiple time scales

TL;DR

This paper introduces a novel multi-state modeling approach that integrates excess hazard estimation and multiple time scales to better assess late effects in cancer survivors, enabling detailed risk partitioning.

Contribution

It combines multi-state models with excess hazard estimation within a relative survival framework, allowing for comprehensive risk analysis of morbidity and mortality in cancer patients.

Findings

Effective partitioning of risks into expected and excess components

Flexible modeling of multiple time scales and transitions

Application to Hodgkin lymphoma data demonstrates utility

Abstract

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multi-state models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, by allowing is to directly quantify the excess risk. This paper proposes a combination of these two frameworks, providing one approach to address (1)-(3). Using recently developed methods in multi-state modeling, we incorporate estimation of excess hazards into a multi-state model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.