Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: which adaptive approach leads to the best result?

Tom Parke, Olga Marchenko, Vladimir Anisimov, Anastasia Ivanova, Christopher Jennison, Inna Perevozskaya, Guochen Song

Research output: Contribution to journalArticlepeer-review

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Abstract

Designing an oncology clinical program is more challenging than designing a single study. The standard approaches have been proven to be not very successful during the last decade; the failure rate of Phase 2 and Phase 3 trials in oncology remains high. Improving a development strategy by applying innovative statistical methods is one of the major objectives of a drug development process. The oncology sub-team on Adaptive Program under the Drug Information Association Adaptive Design Scientific Working Group (DIA ADSWG) evaluated hypothetical oncology programs with two competing treatments and published the work in the Therapeutic Innovation and Regulatory Science journal in January 2014. Five oncology development programs based on different Phase 2 designs, including adaptive designs and a standard two parallel arm Phase 3 design were simulated and compared in terms of the probability of clinical program success and expected net present value (eNPV). In this article, we consider eight Phase2/Phase3 development programs based on selected combinations of five Phase 2 study designs and three Phase 3 study designs. We again used the probability of program success and eNPV to compare simulated programs. For the development strategies, we considered that the eNPV showed robust improvement for each successive strategy, with the highest being for a three-arm response adaptive randomization design in Phase 2 and a group sequential design with 5 analyses in Phase 3.

Original languageEnglish
Pages (from-to)457-476
Number of pages20
JournalJournal of Biopharmaceutical Statistics
Volume27
Issue number3
Early online date7 Feb 2017
DOIs
Publication statusPublished - 2017

Keywords

  • Adaptive trial design
  • decision analysis
  • expected net present value
  • group sequential design
  • modeling and simulation
  • optimizing drug development
  • probability of success

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