OBJECTIVES: Continuous-time multi-state models provide a framework for estimating the probability of an individual transitioning from one health state to another together with time spent in each health state. In cost-utility studies, effectiveness often estimated using quality-adjusted life years (QALYs) over a lifetime horizon. Accurately estimating long-term QALYs is crucial in health technology assessment, but this usually requires extrapolating short-term follow-up data. Results are highly sensitive to how the extrapolation is performed. Survival extrapolation using a relative survival framework (i.e., extrapolating after partitioning the all-cause hazard into the expected hazard and the excess hazard) has proven to be superior to direct extrapolation of all-case survival for estimating life expectancy after a cancer diagnosis. However, this approach has not yet been implemented in multi-state models for predicting QALYs.