Cost-Utility Analysis

Cost-utility analyses represent a special case of a cost-effectiveness analysis where effectiveness is measured in terms of quality-adjusted life years (QALYs). QALYs represent years of life weighted by the quality of life in those years. QALYs have some special properties that are discussed in greater detail in the Outcome Measures section of this chapter. Cost-utility analyses have several additional advantages over cost-effectiveness analyses. Foremost, any medical intervention can be measured in terms of QALYs. Therefore, QALYs represent a standard outcome measure that can be used to compare a variety of medical treatments. Comparing across medical interventions is important for policy and budgetary planning (e.g., a dementia caregiver intervention can be evaluated against a depression intervention). In addition, QALYs combine all outcomes of an intervention into one single measure. This overcomes the challenge associated with cost-effectiveness studies in that multiple cost-effectiveness ratios may be evaluated. For these reasons, cost-utility analysis is considered the gold standard for health economic evaluations. However, QALYs are not without limitations, such as limited movement in this measure during short duration trials (i.e., those lasting <6 months), differences in the precision of QALY measurement instruments, and debates as to whether the weighting values obtained from the instrument development process accurately represent the preferences of the population being studied.

The statistic of interest in cost-effectiveness and cost-utility analyses is the incremental cost-effectiveness ratio (ICER). The ICER represents the price of an additional unit of effectiveness achieved from a new treatment compared to the standard of care. The ICER can also be graphically represented on a Cartesian plane. Figure 18.2 shows a graphical representation of the ICER. In the graph, the x-axis represents the difference in effectiveness between two interventions, and the y-axis represents the difference in cost between two interventions. The graph is divided

Cost-effectiveness plane reproduced from economic analysis alongside randomized controlled trials

Figure 18.2 Cost-effectiveness plane reproduced from economic analysis alongside randomized controlled trials: design, conduct, analysis, and reporting.

Source: Reprinted from Petrou and Gray (2011), with permission from BMJ Publishing Group Ltd.

into four regions (Northeast, Southeast, Southwest, and Northwest). If the ICER of a new treatment compared to the standard of care falls in the Northeast region, then the new treatment is more costly but also more effective. If the ICER falls in the Southeast region, then the new treatment is less costly and more effective than the comparator. ICERs that fall in the Southeast region are said to dominate. If the ICER falls in the Southwest region, then the new treatment is less effective and less costly than the comparator. Finally, ICERs that fall in the Northwest region indicate the new treatment is less effective and more costly than the standard of care.

Importantly, cost-effectiveness does not imply cost savings. A cost-effective intervention can cost more yet be more effective than the comparator (Northeast region, Figure 18.2). However, not all ICERS that fall in the Northeast region are cost-effective. In order for an intervention to be considered cost-effective (Northeast region, Figure 18.2), it must have an ICER that is less than the decision makers’ maximum acceptable ICER (i.e., is the extra cost of the benefit worth it?). The main drawback of cost-effectiveness analysis is a lack of consensus as to what is considered cost-effective or whether the extra cost of the benefit is worth it. In the United States, there is no established societal threshold for determining cost- effectiveness; however, studies typically use $50,000 to $150,000 as a threshold. This range represents the value of a QALY from a societal perspective, and decision makers from different payers or care delivery settings may value a QALY differently (Gold et al., 1996).

 
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