OP34 One-Way Sensitivity Analysis For Cost Effectiveness Analysis. (2019)

• Record Type: Journal Article
• Authors: McCabe, Christopher; Awotwe, Isaac; Paulden, Mike; Sutton, Andrew; Hall, Peter
• Is Part Of: International journal of technology assessment in health care. Volume 35(2019)Supplement 1
• Page Start: 8
• Page End: 8
• Publication Date: 2019
• Subjects: Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S0266462319001041
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : Introduction: Although stochastic analysis has become the accepted standard for decision analytic cost effectiveness models, deterministic one-way sensitivity analysis continues to be used to meet the needs of decision makers to understand the impact that changing the value taken by one specific parameter has on the results of the analysis. However, there are a number of problems with this approach. Methods: We review the reasons why deterministic one-way sensitivity analysis will provide decision makers with biased and incomplete information. We then describe a new method - stochastic one-way sensitivity analysis (SOWSA), and apply this to a previously published cost effectiveness analysis, to produce a stochastic tornado diagram and conditional incremental net benefit curve. We then discuss how these outputs should be interpreted and the potential barriers to the implementation of SOWSA. Results: The results illustrate the shortcomings of the current approaches to deterministic one-way sensitivity analysis. For SOWSA, the expected costs and outcomes are captured, along with the sampled value of the parameter and these are linked to the probability that the parameter takes that value – which can be read off the probability distribution for the parameter used in the stochastic analysis. From these results it is possible to gain insights into probability that a parameter will take a value that will change a decision. Conclusions: Although a well-used technique, one-way deterministic sensitivity analysis has a number of shortcomings that may contribute to incorrect conclusions being drawn about the importance of certain parameter values on model results. By providing fuller information on uncertainty in model results, it is hoped that the methods here will lead to more informed decision making. Although, as with all developments in the presentation of analytic results to decision makers, care will be required to ensure that the decision makers understand the information provided to them.