Economic Modelling

Economic modelling involves creating simplified representations of real-world scenarios to aid decision-making, particularly in the economic evaluation of healthcare interventions. This process synthesizes clinical, epidemiological, and economic evidence from various sources into a structured evaluation framework, aiming to estimate specific outcomes, such as the incremental cost-effectiveness ratio (ICER).

Key elements of economic modelling include:

– Design/Structure: Models can take various forms, such as decision trees, cohort Markov models, micro-simulations, and, less commonly, discrete event simulations. Each design has its unique approach to representing the progression of diseases and the impact of interventions.

– Modelling Assumptions: Assumptions are made to simplify complex real-world processes, such as disease progression and treatment effects. These assumptions help to construct the model but also introduce potential sources of bias.

– Input Parameters: Parameters include data on costs, health outcomes, probabilities of events, and other relevant factors. These parameters are typically derived from clinical trials, observational studies, and expert opinion.

Economic models are used to estimate outcomes by integrating these elements, providing valuable insights into the cost-effectiveness of different healthcare interventions. Sensitivity analysis is crucial in economic modelling, allowing the exploration of how uncertainty in input parameters and model structure affects the outcomes. This analysis helps identify which variables most influence the results and assesses the robustness of the model’s conclusions.

By facilitating a comprehensive assessment of the economic implications of healthcare interventions, economic modelling supports informed decision-making, helping policymakers and healthcare providers allocate resources effectively to optimize health outcomes.

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