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  • RVX-208 Discrete event simulation DES is an alternative

    2019-08-16

    Discrete event simulation (DES) is an alternative modeling technique to which the challenges associated with discrete time cycles do not apply. Events can occur at any time in a DES model, because the time to these events are typically modeled using smooth time-to-event distributions, e.g. Gamma or Weibull distributions [13]. In DES, the behavior of a system is translated into an ordered sequence of well-defined events, which comprise specific changes in the system's state at a specific point in time [13]. DES is well suitable for modeling clinical processes, as it is able to incorporate patient-level characteristics and clinical histories, competing resources, and interactions between different actors, e.g. physicians and patients [14]. Although originating from the operations research field, DES is increasingly being used for cost-effectiveness modeling [15]. Several studies have compared the use of DT-STM and DES for cost-effectiveness analyses of medical technologies. Using the same model structure and evidence, quantitative outcomes such as the incremental cost-effectiveness ratio (ICER), are unlikely to be substantially different between these modeling methods [16,17]. However, substantial differences in outcomes may occur, if the use of DES results in a more appropriate representation of clinical practice compared to DT- STM, for example by including patient characteristics or considering resource constraints [18]. Especially in the scenario in which insufficient observations are available for the chosen RVX-208 length, and irregularities in the cycle-specific transition probabilities are substantial when using DT-STM, the use of DES might be preferable. The objective of this study is to compare the evidence structure and outcomes of a recently published cost-effectiveness DT-STM [19] with those of a newly developed DES model. The comparison will be RVX-208 performed based on the dataset of the randomized clinical phase III CAIRO3 study, in which maintenance treatment with capecitabine and bevacizumab (CAP-B) or observation in metastatic colorectal cancer patients after six induction cycles of capecitabine, oxaliplatin, and bevacizumab (CAPOX-B) was evaluated [20]. The results of one gene study should facilitate a better understanding of the potential impact of selecting a modeling method for cost-effectiveness modeling studies informed by IPD.
    Methods
    Results The replicated DT-STM developed for this study yielded comparable cost-effectiveness outcomes as the original DT-STM developed in a different software environment. The results for the original DT-STM have been previously published elsewhere and are not presented here for the sake of readability [19]. The replicated DT-STM will be referred to just as “DT-STM” in the subsequent part of this manuscript.
    Discussion Cost-effectiveness outcomes were comparable for the DT-STM and the DES model (ICER €172,443 and €168,383, respectively). The rather small differences observed, can be explained by the disparities in simulated mean time to transitions between both models. Furthermore, the magnitude of uncertainty surrounding the mean ICER point-estimate was smaller for the DES model. The observed difference in the uncertainty might be caused by the irregularities in the health state-transition probabilities in the DT-STM, consequently causing more extreme effects compared to the smooth health-state transition curves of the DES model. Results of this study did not alter the previously published conclusion that CAP-B maintenance may not be regarded as cost-effective [19]. These results confirm that cost-effectiveness outcomes are not expected to be substantially different between DT-STM and DES models, if both models are based on the same evidence [16,17]. It is, however, imaginable that ICER outcomes closer to a country’s willingness to pay threshold might incur different conclusions on cost-effectiveness depending on the choice of modeling method. This was previously demonstrated by Jahn et al comparing a DES model and a DT-STM evaluating decision tools for adjuvant chemotherapy treatment in breast cancer [28].