Maximizing Return on Investment in Phase II Proof-of-Concept Trials

Cong Chen (), Robert A. Beckman and Linda Z. Sun
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Cong Chen: Merck Research Laboratories (MRL)
Robert A. Beckman: University of California at San Francisco
Linda Z. Sun: Merck Research Laboratories (MRL)

Chapter Chapter 9 in Optimization of Pharmaceutical R&D Programs and Portfolios, 2015, pp 141-154 from Springer

Abstract: Abstract Phase II proof-of-concept (POC) trials play a key-role in oncology drug development, determining which therapeutic hypotheses will undergo definitive Phase III testing according to predefined Go–No Go (GNG) criteria. The number of possible POC hypotheses likely far exceeds available public or private resources. In this chapter, we propose to find the optimal decisions by explicitly maximizing benefit–cost ratio (aka return on investment), which is often the implicit objective in an otherwise qualitative decision-making process. The numerator of the function, in its simplistic form, represents expected number of truly active drugs identified for Phase III development, and the denominator represents the expected total sample size in the Phase II/III development so that the utility function directly measures how much a patient contributes to the development of an active drug (and its inverse measures how many patients it takes to develop an active drug). The method is easy to explain and simple to implement. Optimization of the benefit-cost ratio leads to type I/II error rates (and therefore sample size) for a trial that is most cost-effective. This in turn leads to cost-effective Go–No Go (GNG) criteria for development decisions. The idea is applied to derive optimal trial-level design strategy which is to conduct more small POC trials with high GNG bars. Although some active indications will be missed due to the higher GNG bar, this is more than compensated for by the reduction in type III error (i.e., the opportunity cost of missing POC trials that might have identified a true positive) inherent in testing more POC hypotheses in the total program. The idea is also applied to derive optimal program-level and portfolio-level design strategy which is to, as expected, allocate more resources to POC trials corresponding to hypotheses with more clinical value, better understanding of the endpoint or stronger scientific support. In the extreme case, the hypotheses with the greatest merit will be tested in larger trials and the some of the weaker hypotheses will not be tested, mirroring the traditional paradigm. Finally, the same idea is applied to the design of a seamless Phase II/III design whereas prior information on relationship between Phase II and Phase III endpoints, and various other practical considerations are incorporated to the cost-effectiveness analyses.

Keywords: Adaptive design; Bayesian analysis; Decision analysis; Early efficacy endpoint; Non-inferiority; Oncology; Seamless design (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-09075-7_9

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