Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

Abstract

Objective The objective of this study was to assess long-term survival outcomes for nivolumab and everolimus in renal cell carcinoma predicted by three model structures, a partitioned survival model (PSM) and two variations of a semi-Markov model (SMM), for use in cost-effectiveness analyses. Methods Three economic model structures were developed and populated using parametric curves fitted to patient-level data from the CheckMate 025 trial. Models consisted of three health states: progression-free, progressed disease, and death. The PSM estimated state occupancy using an area under-the-curve approach from overall survival (OS) and progression-free survival (PFS) curves. The SMMs derived transition probabilities to calculate patient flow between health states. One SMM assumed that post-progression survival (PPS) was independent of PFS duration (PPS Markov); the second SMM assumed differences in PPS based on PFS duration (PPS-PFS Markov). Results All models provide a reasonable fit to the observed OS data at 2 years. For estimating cost effectiveness, however, a more relevant comparison is between estimates of OS over the modeling horizon, because this will likely impact differences in costs and quality-adjusted life-years. Estimates of the incremental mean survival benefit of nivolumab versus everolimus over 20 years were 6.6 months (PSM), 7.6 months (PPS Markov), and 7.4 months (PPS-PFS Markov), reflecting non-trivial differences of + 14% and + 11%, respectively, compared with PSM. Conclusions The evidence from this study and previous work highlights the importance of the assumptions underlying any model structure, and the need to validate assumptions regarding survival and the application of treatment effects against what is known about the characteristics of the disease.

Economic Evaluation of First-Line Atezolizumab for Extensive-Stage Small-Cell Lung Cancer in the US

“…In the economic evaluation of tumor drugs, we usually need to fit the curves from the clinical trials to construct a Markov model or a partitioned survival (PS) model ( 7 ). Even when the data for OS and PFS curves are immature, we still need to extrapolate them based on the fit results.…”

Cost-effectiveness of first-line treatments in metastatic renal cell carcinoma

“…A study by Smare et al, evaluated STM versus PSM in cost-effectiveness in RCC using the competing drug nivolumab as a standalone treatment compared everolimus in patients with previously treated advanced RCC and concluded that different model structures may produce different estimates of QALY that alter cost-effectives conclusions. Furthermore, the STM more closely fit observed three-year survival data for nivolumab in previously treated advanced RCC patients [14]. While the effect of the choice of model in the pembrolizumab / axitinib trial cannot be known, this decision merits further explanation in the methodology and could be a topic of future study.…”

Challenges in synthesising cost-effectiveness estimates

“…decision trees, state-transition models, patient simulations) which should theoretically yield the same conclusion about the cost effectiveness of an intervention, however, due to the diverging use of data and assumptions made results are likely to differ. For example, in oncology, published examples have demonstrated that different model structures estimate different durations spent in key health states, impacting cost-effectiveness results [8][9][10][11][12]. Despite this, in the same disease area, justification for the choice of model structure is often minimal [13].…”