A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology

Aims To construct and compare a partitioned-survival analysis (PartSA) and a semi-Markov multi-state model (MSM) to investigate differences in estimated cost effectiveness of a novel cancer treatment from a UK perspective. Materials and Methods Data from a cohort of late-stage cancer patients (N > 700) enrolled within a randomized, controlled trial were used to populate both modelling approaches. The statistical softwareRwas used to fit parametric survival models to overall survival (OS) and progression-free survival (PFS) data to inform the PartSA (package flexsurv). The package mstate was used to estimate the MSM transitions (permitted transitions: (T1) progression-free to dead, (T2) post-progression to death, and (T3) pre-progression to post-progression). Key costs included were treatment-related (initial, subsequent, and concomitant), adverse events, hospitalizations and monitoring. Utilities were stratified by progression. Outcomes were discounted at 3.5% per annum over a 15-year time horizon. Results The PartSA and MSM approaches estimated incremental cost-effectiveness ratios (ICERs) of 342,474 pound and 411,574 pound, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from +0.15 to +0.33 for the PartSA approach, compared with -0.13 to +0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between 234,829 pound and 522,963 pound, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above 7 pound million (caused by very small incremental QALYs). Limitations and conclusions Structural uncertainty in economic modelling is rarely explored due to time and resource limitations. This comparison of structural approaches indicates that the choice of structure may have a profound impact on cost-effectiveness results. This highlights the importance of carefully considered model conceptualization, and the need for further research to ascertain when it may be most appropriate to use each approach.