Which statement best describes a sensitivity analysis in pharmacoeconomic evaluations?

Which statement best describes a sensitivity analysis in pharmacoeconomic evaluations?

• A. It creates a graphic display of the outcomes of each treatment alternative and the probability of their occurrence
• B. It depends on how the outcomes are measured
• C. It shows how results would change when key assumptions are varied across a plausible range
• D. It lists all anticipated consequences or events that can be measured

Answer: (C)

Sensitivity analysis in pharmacoeconomic evaluations focuses on showing how results change when the key assumptions are varied within plausible ranges. This approach tests the robustness of conclusions, such as the cost-effectiveness or net benefit of a treatment, by adjusting inputs like drug price, effectiveness, adherence, time horizon, and discount rate. By performing one-way analyses (varying one parameter at a time) or probabilistic analyses (varying many parameters simultaneously according to distributions), you identify which factors drive the results and determine threshold values where a decision would change. The statement that best captures this idea is the one that describes showing how results would change as key assumptions are varied across a plausible range. The other descriptions refer to presenting results, how outcomes are measured, or enumerating possible events—functions that do not address testing the impact of uncertainty on conclusions.

Sensitivity analysis in pharmacoeconomic evaluations focuses on showing how results change when the key assumptions are varied within plausible ranges. This approach tests the robustness of conclusions, such as the cost-effectiveness or net benefit of a treatment, by adjusting inputs like drug price, effectiveness, adherence, time horizon, and discount rate. By performing one-way analyses (varying one parameter at a time) or probabilistic analyses (varying many parameters simultaneously according to distributions), you identify which factors drive the results and determine threshold values where a decision would change. The statement that best captures this idea is the one that describes showing how results would change as key assumptions are varied across a plausible range. The other descriptions refer to presenting results, how outcomes are measured, or enumerating possible events—functions that do not address testing the impact of uncertainty on conclusions.