Sensitivity Analyses

Because it is impossible to collect perfect data, an analyst frequently has to make a series of assumptions based on findings from prior studies, expert opinion, educated guesses from

figure 31.1 Decision tree for a cost-effectiveness analysis. This tree is identical to that for the cost-benefit analysis in Chapter 29 (Figure 29.3), except the leafs of the tree are pairs of numbers corresponding to dollar costs (C) and a measure of effectiveness (E) instead of a single number representing net benefit in dollars (cb in Figure 29.3).

figure 31.1 Decision tree for a cost-effectiveness analysis. This tree is identical to that for the cost-benefit analysis in Chapter 29 (Figure 29.3), except the leafs of the tree are pairs of numbers corresponding to dollar costs (C) and a measure of effectiveness (E) instead of a single number representing net benefit in dollars (cb in Figure 29.3).

personal experience, or, in some cases, truly random guesses. The study assumes that available information about costs and other model parameters is accurate, and make estimates for model parameters for which published data are not available. As a result, an economic study is only as strong as the information on which it rests, including its assumptions. For this reason, an organization or other consumer of an economic study must be aware of all assumptions and their accompanying reasons. Although some assumptions may be minor and have little bearing on the study results, others may be very controversial and dramatically influence study conclusions. Sensitivity analyses can help ascertain the impact of these assumptions.

Sensitivity analyses involves changing important variables along a range of different values and measuring the consequent effects on the results. For example, what would happen to the results if the discount rate varied from 2% to 6%, the cost of a specific medication ranged from $100 to $500, the percentage of people receiving a certain test changed from 40% to 60%, or the study excluded certain costs that were previously included? Running these different scenarios will not only identify the variables that have an important impact on the results but also demonstrate the credibility of the economic study. An economic study that does not change significantly is considered "robust"; i.e., most analysts would consider its results definitive. However, an economic study with results that fluctuate significantly during sensitivity analyses is not necessarily useless. The sensitivity analyses can help target the items and issues that are most responsible for the costs and rewards of a situation. If, for example, if the results of a study depend heavily on medication costs, then one may make extra efforts to either reduce the cost of medications or find alternative treatments.

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