A Practical Method for Determining the Optimal Allocation between Computation Time and Accuracy in Probabilistic Patient-Level Microsimulation Models


Szafranski K1, Disher T2, Brown S1
1EVERSANA, Burlington, ON, Canada, 2EVERSANA, West Porters Lake, NS, Canada

OBJECTIVES: For Health Technology Assessment (HTA) bodies, the use of probabilistic modeling increasingly informs decision making. However, running probabilistic patient-level microsimulations in HTA-accepted software like Excel requires substantial computation time. The objective of this analysis is to determine a practical method for optimizing computational time for multifactorial microsimulations.

METHODS: Previous work suggests that computation time can be optimized using equations involving precision required (c2), and k, a ratio of mean patient-level variance and probabilistic run variance of incremental net benefit (INB). To calculate these parameters, a set of 3d probabilistic runs is required, where d is the number of probabilistic inputs. However, this is infeasible in microsimulation models that have more than 5 inputs. Instead, use of one-way sensitivity analysis (OWSA) to select the two or three most impactful variables is proposed. The model used to test this approach was a diabetes microsimulation model comparing two therapies A and B.

RESULTS: A OWSA revealed that two input parameters that most impacted the estimate of INB were the difference in efficacy between A and B and the number of cost units of B. Creating a 3X3 matrix with microsimulation runs of n = 100,000 patients for each variable run as mean or mean+/- 1.5 SD allowed for calculation of k. This resulted in an optimal n of 18,556 and an optimal number of probabilistic simulations (N) of 3,200 for c2 = 0.05. However, the run-time for these parameter values would be over two months in Excel using a four-core machine with 16Gb of RAM.

CONCLUSIONS: Selecting the top variables is a practical approach to determine optimal n and N for probabilistic microsimulations. However, the use of Excel to run these analyses is impractical, and use of languages such as R may be necessary.

Conference/Value in Health Info

2023-05, ISPOR 2023, Boston, MA, USA

Value in Health, Volume 26, Issue 6, S2 (June 2023)




Economic Evaluation, Methodological & Statistical Research, Study Approaches

Topic Subcategory

Cost-comparison, Effectiveness, Utility, Benefit Analysis, Decision Modeling & Simulation


No Additional Disease & Conditions/Specialized Treatment Areas

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