20.7 Microsimulation

In decision analysis, the most efficient calculation is to use expected values, as described in the section Build a Legal Model and illustrated in this section. However, it is also possible to evaluate decision trees using individual-level simulation, sometimes referred to as microsimulation.

Microsimulation in decision trees approximates an expected value by “sampling” a representative distribution of paths through the model’s chance events. Microsimulation of complex models generally utilizes as many “trials” as time allows, in order to improve the EV estimation (ensuring even small probability paths are “sampled” proportionally). If run at a decision node, each trial is repeated for each strategy, to facilitate strategy comparison (e.g., CEA).

• Refer to the chapters on Microsimulation for further details on Monte Carlo simulation, including details on running a Monte Carlo Probabilistic Sensitivity Analysis.