title: Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: Which adaptive approach leads to the best result?
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.
authors.affiliation: a Berry Consultants , Abingdon , Oxfordshire , UK.
authors.forename: Tom
authors.initials: T
authors.lastname: Parke
authors.affiliation: b Advisory Services Analytics, Quintiles , Durham , North Carolina , USA.
authors.forename: Olga
authors.initials: O
authors.lastname: Marchenko
authors.affiliation: c School of Mathematics and Statistics, University of Glasgow , Glasgow , UK.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Anisimov
authors.affiliation: d Department of Biostatistics , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA.
authors.forename: Anastasia
authors.initials: A
authors.lastname: Ivanova
authors.affiliation: e Department of Mathematical Sciences , University of Bath , Bath , UK.
authors.forename: Christopher
authors.initials: C
authors.lastname: Jennison
authors.affiliation: f Statistical Research and Consulting Center, Pfizer, Inc. , Collegeville , Pennsylvania , USA.
authors.forename: Inna
authors.initials: I
authors.lastname: Perevozskaya
authors.affiliation: b Advisory Services Analytics, Quintiles , Durham , North Carolina , USA.
authors.forename: Guochen
authors.initials: G
authors.lastname: Song
mesh_terms: D000075522:Adaptive Clinical Trials as Topic; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D006801:Humans; D008495:Medical Oncology; D011336:Probability; D016032:Randomized Controlled Trials as Topic; D012107:Research Design
keywords: Adaptive trial design; decision analysis; expected net present value; group sequential design; modeling and simulation; optimizing drug development; probability of success