Addressing the Challenge for Well Informed and Consistent Reimbursement Decisions

Abstract

In the context of ever-increasing healthcare expenditures and resource-limited health services, the comparative analysis of costs and benefits of competing healthcare technologies (e.g. pharmaceuticals) is essential to support public funding decisions. Australia has been at the forefront of the integration of economic evidence with reimbursement decisions to fund new pharmaceutical products through a national-level body, the Pharmaceutical Benefits Advisory Committee (PBAC). Pharmaceutical companies submit applications to have their products considered for reimbursement. The PBAC recommendations form the basis of Australian Government decisions about public funding and listing of medicines on the Pharmaceutical Benefits Scheme. The UK National Institute for Health and Clinical Excellence (NICE), amongst others, has adopted guidelines to use economic evidence for reimbursement decision processes. Decision analytic models are an expected framework for the economic evaluation of healthcare technologies submitted to national regulatory bodies such as the PBAC and NICE for public funding. They provide an explicit process for synthesising data from a variety of sources, linking intermediate outcomes to final endpoints (e.g. QALYs), extrapolating beyond the data observed in a clinical trial, and allowing for the appropriate handling and representation of uncertainty around outputs. It is now well accepted that uncertainty is inherent within any model-based economic evaluation and needs to be handled appropriately so that policy makers can have confidence in themodel’s results and/ormake decisions regarding the need for additional information. In model-based evaluations, the sources of uncertainty have been classified into three broad categories. Parameter uncertainty concerns models’ input values (e.g. probabilities of moving between states, resource use estimates) and the fact that the true values of most parameters are unknown. A further source of uncertainty, methodological, relates to the choice of analytic methods such as the perspective taken (e.g. society, government). Structural uncertainty arises from the assumptions imposed by the modelling framework and refers to the structure of the chosen model, i.e. the choice of clinical events represented in a model, and the possible transitions between them. Issues around parameter and methodological uncertainties are generally well understood and continually refined in guidelines developed by the national reimbursement bodies such as the PBAC and NICE (e.g. sensitivity analysis to address parameter uncertainty and using a ‘reference case’ to deal withmethodological uncertainty). Although concerns have been raised regarding the impact of assumptions incorporated in model structures on the quality of models, and conjecture that structural uncertainty may have a greater impact on the model’s results than other sources of uncertainty, relatively little guidance has been EDITORIAL Pharmacoeconomics 2011; doi: 10.2165/11593000-000000000-0000