To provide a strong infrastructure for good modeling practice considering:
1. Clinical realism: A model should reflect the state of evidence, the current understanding of the disease, and be accepted by clinical experts.
2. Quantifying decision uncertainty: A model should be capable of quantifying decision uncertainty and informing prioritization of future research.
3. Transparency and reproducibility: Resources should exist so that a model can be completely understood, reproduced, and pressure tested.
4. Reusability and adaptability: It should be possible to easily update a model to reflect new clinical evidence or adapt it for a new market, indication, or intervention.
To support our clients for a more efficient introduction of new products to their target groups such as policymakers, clinicians, and patient organizations.
To provide a quick updating process for the economic model when a new set of input data are available.
To enhance model adaptation for multiple countries all in one platform.
The system consists of two main parts: server-side and client-side. Model engines are developed with the support of R, Python, or Excel on the server-side.
Depending on the values users submit to the system, the server-side runs the analysis and generates the results. The client-side provides a graphical user interface (GUI) for users to interact with the platform and make appropriate adjustments to the model settings and input values.
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