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NotificationsNotify me of updates to SYNTHESIS OF "STATISTICAL INNOVATIONS FOR COST-EFFECTIVENESS ANALYSIS" TRANSLATING RESEARCH INTO POLICY AND PRACTICE (TRIPP) pp. 121-151
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SYNTHESIS OF "STATISTICAL INNOVATIONS FOR COST-EFFECTIVENESS ANALYSIS" TRANSLATING RESEARCH INTO POLICY AND PRACTICE (TRIPP) pp. 121-151 $100.00
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. 


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SYNTHESIS OF "STATISTICAL INNOVATIONS FOR COST-EFFECTIVENESS ANALYSIS" TRANSLATING RESEARCH INTO POLICY AND PRACTICE (TRIPP) pp. 121-151