• 2019-06
  • 2018-12
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-08
  • 2021-03
  • RVX-208 Discrete event simulation DES is an alternative


    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.
    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].