METHOD MATTERS: PARTITIONED SURVIVAL MODELS CHARACTERIZE AND EXTRAPOLATE RISKS DIFFERENTLY FROM MARKOV MODELS

Purpose: To illustrate differences in assumptions between partitioned survival and Markov model approaches for state-based economic evaluation using an example from a recent reimbursement review.

Method: We developed a three-state economic model comparing bevacizumab + capecitabine (new strategy) to capecitabine alone (comparator) based on the results of the AVEX trial (Cunningham et al 2013). The three model states were progression-free, progressed and dead and were populated using both partitioned survival and Markov modelling approaches. Since patient-level data were not available, we recreated patient-level data using the methods of Guyot et al (2012) and fit parametric distributions to inform survival estimates. We also sought external study sources to estimate Markov transition probabilities for death from the intermediate state of progression, which in the absence of further data, was assumed to be the same regardless of initial treatment strategy. We contrast the data and assumptions used to populate each model type and demonstrate the implications for the estimated extra costs and effects produced.

Result: The partitioned survival model and Markov model produced similar incremental costs ($53,209, $53,902, respectively), and each produced 0.263 QALY gains in the progression-free state for the new strategy. Overall, the partitioned survival analysis produced an incremental 0.186 QALYs for the new strategy, due to 0.077 QALYs lost in the progressed state. In contrast, the Markov model produced an incremental 0.245 QALYs, due to only 0.018 QALYs lost in the progressed state. These differences led to ICERs of $286,121 and $220,027 per QALY gained for partitioned survival and Markov models, respectively. In the AVEX trial, PFS gain was larger than the OS gain. The partitioned survival model outcomes appeared to accurately reflect the trial data, while the Markov model survival did not align well with overall survival from the trial without further calibration and assumptions.

Conclusion: Uncertainties about partitioned survival models have often been accompanied by recommendations to pursue a Markov model. Both partitioned survival models and Markov models have limitations that can affect their ability to accurately reflect clinical reality. In this example, we demonstrated a scenario in which a commonly used approach for Markov models can lead to results that overestimate treatment benefits. Our study results suggest it is appropriate to consider both modelling types to address uncertainty in an analysis because they characterize and extrapolate risks differently.