The dominant approaches to modeling the cost effectiveness of immuno-oncology (IO) therapies in advanced non-small cell lung cancer are Markov and partitioned survival models. There is substantial variability in the handling of related methodological challenges.

The lack of long-term trial results for IO therapies led to the use of real-world data for survival extrapolation and ad hoc assumptions related to long-term benefits of IO. A treatment effect lasting for 3 or 5 years after the initiation of the therapy was commonly assumed.

For utility modeling, health state-based utilities and time-to-death models are widely used.