  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.
