Monte-Carlo Simulation

Monte-Carlo simulation is a modeling technique used across various scientific disciplines where model inputs are drawn from probability distributions rather than being treated as fixed values. This method is particularly useful for capturing uncertainty and variability in model predictions. The key elements of a Monte-Carlo simulation include:

  1. Defining the Domain of Possible Inputs: Identify the range and types of input parameters.
  2. Generating Input Values Randomly: Draw input values from specified probability distributions within the defined domain.
  3. Performing Deterministic Computations: Use the randomly selected inputs to compute the model output.
  4. Repeating the Process: Conduct a sufficient number of iterations, or ‘draws’, of input values.
  5. Aggregating Results: Compile the outcomes from all iterations to analyze the distribution of the model output.

In healthcare evaluations, Monte-Carlo simulations often underpin micro-simulations, where probability distributions are used to create cohorts of patients with varying risk factors that influence their future health outcomes. Probabilistic sensitivity analysis (PSA) is a specific application of Monte-Carlo simulation in health economics, where parameter values are varied stochastically to estimate the distribution of the model’s output value, providing insights into the robustness and reliability of the results.

Monte-Carlo simulation is valuable for exploring the impact of uncertainty in model parameters and for generating a range of possible outcomes, thereby supporting more informed and robust decision-making in healthcare and other fields.