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

Abstract

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 software R was 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 and £411,574, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from þ0.15 to þ0.33 for the PartSA approach, compared with À0.13 to þ0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between £234,829 and £522,963, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above £7 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.

Cost-Effectiveness of Lenvatinib Plus Pembrolizumab or Everolimus as First-Line Treatment of Advanced Renal Cell Carcinoma

“…The model was constructed using a PS structure with certain advantages, which is a well-established modeling approach to simulate metastatic RCC disease progression and death ( 19 – 21 ). Compared to the Markov model, PS model does not need to make assumptions about the probability of metastasis; only two outcomes, progression-free survival and overall survival, are required to inform on health status occupancy, and the time to progression status was inferred by the difference between the two outcomes ( 34 ). PS model can more accurately model disease events, avoid natural mortality, and access individual patient data.…”

Out of Date or Best Before? A Commentary on the Relevance of Economic Evaluations Over Time

“…If the characteristics of the model structure mean that more sophisticated modelling approaches are required, it is unlikely that the results can be considered comparable to those generated by simpler models. A comparison of partitioned survival analysis and state-transition models in cancer found that the choice of model structure had a large impact on cost-effectiveness results [50]. There have also been changes to the recommended methods for trialbased cost-effectiveness analysis, such as how missing data are addressed [51].…”

Investigation of multiple nosocomial infections using a semi-Markov multi- state model

“…However, the semi-Markov MSM is more flexible and does not require the no aftereffect property. The future state of the disease is determined by the sojourn time of the current state (20). When transitioning to the next state, the sojourn time is reset to 0, aligning better with the characteristics of NI occurrence and development.…”