One-Way Sensitivity Analysis for Probabilistic Cost-Effectiveness Analysis: Conditional Expected Incremental Net Benefit

• Published in: PharmacoEconomics Vol. 38; no. 2; pp. 135 - 141
• Main Authors: McCabe, Christopher, Paulden, Mike, Awotwe, Isaac, Sutton, Andrew, Hall, Peter
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2020; Springer; Springer Nature B.V
• Subjects: Cost analysis; Cost benefit analysis; Cost-Benefit Analysis - methods; Decision making; Decision Support Techniques; Economic models; Evaluation; Expected values; Health Administration; Health Economics; Learning models (Stochastic processes); Medical care, Cost of; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Methods; Models, Economic; Monte Carlo Method; Monte Carlo simulation; Pharmacoeconomics and Health Outcomes; Practical Application; Probability; Public Health; Quality of Life Research; Sensitivity analysis; Technology assessment; Technology, Pharmaceutical - economics; Canada
• ISSN: 1170-7690; 1179-2027; 1179-2027
• DOI: 10.1007/s40273-019-00869-3

Although probabilistic analysis has become the accepted standard for decision analytic cost-effectiveness models, deterministic one-way sensitivity analysis continues to be used to meet the need of decision makers to understand the impact that changing the value taken by one specific parameter has on the results of the analysis. The value of a probabilistic form of one-way sensitivity analysis has been recognised, but the proposed methods are computationally intensive. Deterministic one-way sensitivity analysis provides decision makers with biased and incomplete information whereas, in contrast, probabilistic one-way sensitivity analysis (POSA) can overcome these limitations, an observation supported in this study by results obtained when these methods were applied to a previously published cost-effectiveness analysis to produce a conditional incremental expected net benefit curve. The application of POSA will provide decision makers with unbiased information on how the expected net benefit is affected by a parameter taking on a specific value and the probability that the specific value will be observed.