OBJECTIVES: Budget impact analyses (BIA) are a tool for assessing the impact of changes in a given treatment mix on healthcare payers' budgets when introducing a new intervention or increasing the market share of a particular product. When the treatment mix consists of only two interventions, uncertainty in a BIA primarily stems from the future market share of a new intervention. In contrast, appraisals involving multiple interventions introduce additional uncertainty about which existing treatments will be replaced and in what order. Current international guidelines typically address this uncertainty in a deterministic manner, which provides a limited view on the degree of uncertainty. To address these limitation, we developed a framework that quantifies the uncertainties associated with both future market shares and substitution patterns.

METHODS: We propose a parameter space where boundaries are set by the gradual substitution of interventions from most to least expensive, and least to most expensive. Within these boundaries, treatment options have different likelihoods of being substituted. We performed a hypothetical BIA incorporating ten therapies likely of being substituted by a new intervention. The upper and lower bounds of the budget impact across various market shares were determined as described. Using Monte Carlo simulation, expected market share estimates were derived from a beta distribution, while substitution patterns were modeled using a Dirichlet distribution that was adjusted iteratively.

RESULTS: The developed framework maps the most likely market share and resulting budget impact within the defined parameter space, incorporating decision-makers’ preferences for substitution. The Monte Carlo analysis allows us to quantify the range of most likely outcomes.

CONCLUSIONS: Industry stakeholders should be involved in the assessment of the potential applications of our model and the discussion whether it could serve as a useful supplement for national and international BIA guidelines.