The ISPOR task force guidelines on good research practices for Budget Impact Analyses (BIA) identify two simultaneous processes affecting the marketplace: changes in the mix and evolution of available interventions over time (i.e., market share) and changes in the target population resulting from various disease characteristics (e.g., incidence, progression and death). In many chronic diseases, disease severity changes over time and thus medical costs vary across disease cohorts. Hence, in a budget impact model two constraints must be met: 1) the number of patients progressing from one year to the next corresponds to known disease statistics (i.e., patients who enter the model cannot be ‘lost to follow-up’), 2) the total number of treated patients conforms with known population size and projected market shares. The current guidelines lack detail on how to satisfy these two constraints simultaneously in dynamic markets with non-trivial rates of patient attrition from treatment groups.   Objective: To identify a method that allows researchers to more accurately model  the budget impact of new interventions for chronic diseases in dynamic markets. Methods: We propose a simple adjustment factor which is a function of disease and treatment’s attrition rate in two consecutive years to correct the allocation of patients across disease cohorts such that  the two constraints identified above are always met simultaneously. We compare two settings (static vs. dynamic markets) and analyze the implications over a time period of five years. Results: We find that applying the adjustment factor in dynamic markets reduces the bias in budget impact measures by 15% or more and contend that not correcting for this in more complex markets would lead to higher bias. Our proposed solution is a simple way of accounting for differential rates of attrition across treatments in a chronic disease setting in budget impact analyses.