BACKGROUND: Pharmaceutical portfolios are optimized by improved allocation of a fixed budget into individual trials that leads to an improved value of a portfolio. This paper investigates how flexibility of adaptive design contributes to portfolio optimization. METHODS: An example portfolio was designed, and strategies that did or did not include trials with adaptive designs were specified. Operating characteristics of a traditional portfolio were compared to that of an adaptive portfolio. Adaptive portfolios offer potential advantages over traditional ones. Its flexibility largely increases the number of decision points, and as such it allows for a much more frequent reassessment of portfolios. Additionally, an adaptive portfolio can correct itself if initial decisions were made incorrectly. RESULTS: Despite all these advantages, the adaptive portfolio did not outperform the traditional portfolio. The main reason is that in this case, adaptive designs allowed for increases in sample size to the point where improvements per unit increase were minimal, instead of allocating this budget to additional trials. CONCLUSIONS: It is critical to minimize missed opportunities to initiate new promising trials, and to increase sample size only in regions that promise meaningful improvements in power.
BACKGROUND: Pharmaceutical portfolios are optimized by improved allocation of a fixed budget into individual trials that leads to an improved value of a portfolio. This paper investigates how flexibility of adaptive design contributes to portfolio optimization. METHODS: An example portfolio was designed, and strategies that did or did not include trials with adaptive designs were specified. Operating characteristics of a traditional portfolio were compared to that of an adaptive portfolio. Adaptive portfolios offer potential advantages over traditional ones. Its flexibility largely increases the number of decision points, and as such it allows for a much more frequent reassessment of portfolios. Additionally, an adaptive portfolio can correct itself if initial decisions were made incorrectly. RESULTS: Despite all these advantages, the adaptive portfolio did not outperform the traditional portfolio. The main reason is that in this case, adaptive designs allowed for increases in sample size to the point where improvements per unit increase were minimal, instead of allocating this budget to additional trials. CONCLUSIONS: It is critical to minimize missed opportunities to initiate new promising trials, and to increase sample size only in regions that promise meaningful improvements in power.
Authors: M R Trusheim; A A Shrier; Z Antonijevic; R A Beckman; R K Campbell; C Chen; K T Flaherty; J Loewy; D Lacombe; S Madhavan; H P Selker; L J Esserman Journal: Clin Pharmacol Ther Date: 2016-10-19 Impact factor: 6.875