Using the incremental net benefit framework for quantitative benefit–risk analysis in regulatory decision-making—A case study of alosetron in irritable bowel syndrome
Lynd, L. D., Najafzadeh, M., Colley, L., Byrne, M. F., Willan, A. R., Sculpher, M. J., ... Hauber, A. (2009). Using the incremental net benefit framework for quantitative benefit–risk analysis in regulatory decision-making—A case study of alosetron in irritable bowel syndrome. Value in Health, 13(4), 411-417. DOI: 10.1111/j.1524-4733.2009.00595.x
Objective: There is consensus that a more transparent, explicit, and rigorous approach to benefit–risk evaluation is required. The objective of this study is to evaluate the incremental net benefit (INB) framework for undertaking quantitative benefit–risk assessment by performing a quantitative benefit–risk analysis of alosetron for the treatment of irritable bowel syndrome from the patients' perspective.
Methods: A discrete event simulation model was developed to determine the INB of alosetron relative to placebo, calculated as "relative value-adjusted life-years (RVALYs)."
Results: In the base case analysis, alosetron resulted in a mean INB of 34.1 RVALYs per 1000 patients treated relative to placebo over 52 weeks of treatment. Incorporating parameter uncertainty into the model, probabilistic sensitivity analysis revealed a mean INB of 30.4 (95% confidence interval 15.9–45.4) RVALYs per 1000 patients treated relative to placebo over 52 weeks of treatment. Overall, there was >99% chance that both the incremental benefit and incremental risk associated with alosetron are greater than placebo. As hypothesized, the INB of alosetron was greatest in patients with the worst quality of life experienced at baseline. The mean INB associated with alosetron in patients with mild, moderate, and severe symptoms at baseline was 17.97 (?0.55 to 36.23), 29.98 (17.05–43.37), and 35.98 (23.49–48.77) RVALYs per 1000 patients treated, respectively.
Conclusions: This study demonstrates the potential utility of applying the INB framework to real-life decision-making, and the ability to use simulation modeling incorporating outcomes data from different sources as a benefit–risk decision aid.