SYNTHESIS OF "STATISTICAL INNOVATIONS FOR COST-EFFECTIVENESS ANALYSIS" TRANSLATING RESEARCH INTO POLICY AND PRACTICE (TRIPP) pp. 121-151
Authors: (Melford J. Henderson, CFACT, Agency for Healthcare Research & Quality, Division of Socio-Economic Research, Center for Financing, Access, and Cost Trends, Rockville, MD)
Abstract: Rapid increases in health care costs continue to be of significance to public, federal and state agencies, as well as private industry. Publicly funded insurance programs such as Medicare and Medicaid are continually challenged with difficult decisions in allocating health care dollars. Private industries are similarly challenged in providing adequate health care benefits to their employees. The need to contain health care costs forces us to consider which interventions produce the greatest value. Cost-effectiveness analysis (CEA) offers a structured approach for determining economic evaluations of health care programs. It can be used for optimizing health benefits from a specified health care budget, or in finding the lowest cost strategy for a specific health benefit. CEA has also been promoted as a useful tool in the effort to prioritize expenditures on health care programs. By quantifying the trade-offs between resources that need to be deployed and health benefits that accrue from use of alternative interventions, CEA offers guidance in decision-making by structuring comparisons between these interventions. The Agency for Healthcare Research and Quality has funded investigator-initiated research projects for promoting developments related to Translating Research into Policy and Practice. This article summarizes the work led by Joseph Gardiner and colleagues. The goals of this research were to develop new statistical methods that fill methodological gaps, and resolve inconsistencies in CEA. Adopting a framework in which both costs and benefits are stochastic in nature, the research team describes summary measures used in CEA, such as the cost-effectiveness ratio as functions of parameters in an underlying stochastic model. In estimation of these summary measures, the inherent variability in the estimates can be quantified. Markov models provide a probabilistic description of the evolution of events in patients through different health states. In this longitudinal framework, Gardiner et al. use stochastic models that reflect the experience of patients in sustained and changing states of health. Costs are incurred in random amounts at random points in time during the course of an intervention. By compiling these expenditure streams at the individual level into costs per unit time of sojourn in a health state, and in transition between health states, Gardiner et al. estimate the net present value of all expenditures. Health outcome measures such as life expectancy and quality-adjusted life years can also be estimated. In summary, several aspects and complexities in the analyses of health care costs and outcomes are incorporated into these models. The teamís work is continuing. Their methods promise useful applications in CEA.