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The Official News & Technical Journal Of The International Society For Pharmacoeconomics And Outcomes Research


Designing and Developing Budget Impact Models Suited for Global Adaptation

Timothy W. Smith BA and Jonothan C. Tierce CPhil, ValueMedics Research, LLC, Falls Church, VA, USA

This is a summary of a workshop given at the ISPOR 10th Annual International Meeting, May 16, 2005, Washington, DC, USA.

Health care decision-makers are increasingly using Budget Impact Models (BIMs) to evaluate the financial impact of adding a new pharmaceutical product to a formulary. At least 12 countries, including Australia, Belgium, Canada, China, England, Hungary, Israel, Italy, Poland, Switzerland, the United States, and Wales, either require a financial impact analysis (FIA) with reimbursement submissions or issue guidelines for conducting such analyses [1]. Further evidence of this trend is the recent formation of the ISPOR Budget Impact Analysis Task Force, whose stated mission is to create a single set of methodological guidelines to ensure that information on budget impact is developed in a format useful to reviewers [2].

At the same time, manufacturers are currently adopting an integrated, global perspective in product licensing and development strategies. Outcomes research is employed to demonstrate medical need and burden of illness, health economics determines the “value matrix” across the global market, and combined evidence from these two disciplines is employed in the development of a global pricing strategy. And yet, pricing and market access decisions still play out at local level, which means manufacturers are forced to deal with the complexities inherent with simultaneous or sequential presentation of fiscal impact information to multiple reimbursement authorities in different countries. This heightens the need for a modeling framework that is easily adaptable to local market conditions.

Researchers and research sponsors undertaking global BIM development can benefit from an understanding of certain practices that will improve the chances of a successful modeling effort. These practices focus on methods employed in the design, development, and implementation stages of model development that improve the ease with which BIMs can be readily adapted for use with multiple markets (or payment authorities). The definition of success is variable and may be defined as accomplishing internal utilization, attaining publication in a peer-reviewed journal, or achievement of reasonable market access. Regardless, the key question impacting the need for optimization for adaptation is “on what scale?” Any answer besides a single market suggests the need to consider optimizing the model for adaptation. One significant exception to this single market criterion is a model that is to be developed exclusively for use in the United States. Such projects could benefit from optimization techniques so that they will conform to current AMCP guidelines that call for developing plan-specific models to the extent practical [3].

Developing BIMs
A BIM measures the net cumulative cost of treatment with a particular therapy for a given number of patients in a specific population. This is accomplished by implementing comparative cost-determination analyses for competing scenarios, both including and excluding the product of interest. Although a BIM is traditionally focused on pharmacy expenses, the analysis may consider direct medical costs as well. An ideal model framework provides decision-makers with the ability to easily customize the analysis with data from the health system’s own population, as available, in order to develop the most accurate idea of the economic merits of reimbursing the target drug. A well-designed BIM will interactively facilitate examination of different scenarios or therapy regimens by allowing the comparison of competing treatments within a therapeutic category, or, if required, across multiple therapeutic categories displaced by the therapy of interest. The model should walk the user through the analysis by means of a logical progression through the various screens (Figure 1). The flow typically moves from informational screens to an initial set of inputs and calculations, and on to an appropriate set of outcomes. Each screen in the progression should be adequately documented and presented in a comprehensible size. Finally, the model should allow the user to easily conduct sensitivity analyses on relevant parameters. BIMs are typically employed for value communication and negotiation activities. In this capacity they can be used to facilitate an “across the table” discussion between manufacturers and those monitoring health care budgets (typically pharmacy budgets). If the model is well designed, the two parties should be able to easily identify what has been included and excluded and to quickly zero in on the key parameters and underlying assumptions influencing the calculations. A BIM may also be used by a manufacturer for value determination earlier in product lifecycle to assist in determining product value and to inform pricing decisions.

Manufacturer strategies for developing a BIM are most often aimed at demonstrating cost minimization or low budget impact. Cost minimization situations occur when a product is less costly and equally effective as comparators. When cost minimization is the goal, a BIM can be developed and deployed as a single analysis. Low budget impact may be demonstrated when a product is more costly than comparators, but where that incremental cost may be offset by increased effectiveness benefit or cost offsets, or where the product is utilized by a small portion of the population. When demonstrating low budget impact is the goal, the model may require that a cost-effectiveness analysis (CEA) be conducted in conjunction with the BIM, with the BIM building on top of the CEA and scaling the results to the relevant population. BIMs can also be used to examine what tradeoffs need to be made to give access to a drug within a single budget.






Optimizing BIMs for Adaptation

There are numerous challenges in developing any model, and specific challenges in optimizing a model for adaptation. These challenges include ensuring correctness, providing clarity, facilitating communication, reducing complexity, and enabling commonality (Table 1). While it is important to address all challenges, complexity and commonality are the critical elements when designing for adaptation. Techniques drawn from the field of medical informatics are particularly well suited to addressing these challenges (medical informatics is a multidisciplinary field dealing with biomedical information, data, and knowledge, and associated methods of storage, retrieval, and optimal use for problem solving and decision making).

Correctness equates to model validity, which is a requirement for any modeling effort. Correctness can be achieved by following accepted practices for model development and ensuring that adequate processes for quality assurance are in place, and it is assumed for purposes of this discussion. In addition to the ISPOR Budget Impact Analysis Task Force, there are several excellent references on this topic, including the ISPOR report on good research practices [4] and the HTA report on good practice in decision analytic modeling [5].

Clarity is necessary in any modeling effort regardless of adaptation plans, particularly when undertaking value communication or negotiation Model clarity can be achieved by:

  • Organizing the model in logical sequence,

  • Providing full visibility to calculations,

  • Referencing all default values and assumptions, and

  • Providing detail and summary views where needed.

  • Communication is important in any project, and critical in global adaptation efforts. Adequate communication should be perused up front to determine design requirements, during development to ensure adequate feedback, and during implementation to insure adequate training. Strong communication can prevent “scope creep” that can hinder a project’s progress. Complexity is a rate-limiting factor in adaptation efforts. Model complexity can be reduced through the use of modular design and data abstraction. There a link between complexity and clarity. Paradoxically, introducing complexity in the development process can result in improved clarity in the final product. Examples of this phenomenon include the use of drill-down functionality and the addition of navigational features to facilitate logical flow. It is best to keep your goal in mind and to resist the temptation to over-engineer.

    Commonality should be the rule of thumb for global adaptation efforts to the extent that it makes sense. Keep in mind there may be an exception to even the cleverest rule since not all factors influencing BIM are entirely logical. Nevertheless, efforts

    to incorporate commonality in the model framework can facilitate model adaptation efforts. Documentation plays a critical role here, as it can serve as a template for adaptation.

    Case Study #1
    This case study deals with examining the fiscal impact of treating a rare chronic condition with a new medication. This type of modeling exercise can be challenging because of a scarcity of literature in low prevalence therapy areas, a problem that can be compounded by the use of off-label treatments in standard care. Challenges include difficulty in estimating population size and in modeling treatment patters where treatment may be considered as more art than science. It may seem counterintuitive, but introducing a moderate amount of complexity into the design process can actually help to answer key questions. This is especially true when there is uncertainty involved, either because of a lack of documentation or because of variability in treatment patterns in different locales.

    Good design practice can improve the process of model development in general, and it is the key to ensuring that the model will be easily adaptable. One technique that can be employed is the use of a modular design. In developing a population module, it is possible to allow for a flexible and dynamic approach in estimating population (Figure 2). As long as patient subgroups are established up front and defined as module outputs, the calculations within the module can be changed without impacting model calculations downstream. Another sound approach is the use of data abstraction in defining a treatment regimen. By defining the required inputs for a regimen, it makes it easier to allow user definition of the regimens (Figure 3). Judicious use of structure can assist in framing the problem as well as facilitating later adaptation efforts.

    Case Study #2
    This case study deals with developing a BIM to examine the fiscal impact of drug therapy for a highly prevalent condition treated with potentially complex regimens involving both branded and generic drugs from different classes. This exercise can be challenging because although there may be therapeutic guidelines in place, they may vary from country to country. In addition, when dealing with complex drug regimens, including combination therapy involving agents from multiple classes, there is frequently logic involved that dictates valid and invalid combinations, lines of therapy, and potentially therapeutic equivalence. In dealing with such complexity, design is a critical step in the process of developing a useful BIM that will be readily adaptable, and it is worth investing in the effort up front so that multiple exercises can be avoided.

    One useful technique is the use of view-level data abstraction to allow the user to quickly grasp the problem at hand while maintaining their ability to drill-down to view expanded detail (Figure 4A and 4B). In implementing this technique, it is critical to maintain full access to all underlying data and calculations. In addition to making it easy to quickly select relevant comparators while presenting invalid therapeutic options, this powerful technique can be used to facilitate the type of “what-if” exploration to review base- and worse-case scenarios. Another useful technique is the creation of a data checklist to outline data requirements for customizing the model. While it is possible to create a core model with a credible default scenario, customization is almost always required in order to incorporate relevant pricing, market share, and prevalence figures, and good documentation can facilitate this process.

    In conclusion, it should be noted that structure does not obviate the need for substance. Most challenges to BIM development are challenges regardless of plans for adaptation, but addressing certain challenges is more critical when considering global adaptation. By employing techniques drawn from the field of medical informatics, it is possible to improve the changes of overcoming these barriers. For guidance, ask yourself these questions: “Can this be simplified?” and “Is it worth simplifying?”

    1. ISPOR CONNECTIONS. Pharmacoeconomic Guidelines around the World Comparative Table. Last accessed on January 12, 2006: .
    2. ISPOR CONNECTIONS. ISPOR Budget Impact Analysis Task Force. Last accessed on January 12, 2006: 
    3. The AMCP Format for Dossier Submissions Version 2.1. Last accessed on January 12, 2006: Final.pdf
    4. Weinstein MC, O’Brien B, Hornberger J, et al. ISPOR Task Force on Good Research Practices--Modeling Studies. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices-- Modeling Studies. Value Health 2003;6:9-17.
    5. Philips Z, Ginnelly L, Sculpher M, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Asses 2004;8(36).

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