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

Parke, T., Marchenko, O., Anisimov, V., Ivanova, A., Jennison, C., Perevozskaya, I. and Song, G. (2017) Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: which adaptive approach leads to the best result? Journal of Biopharmaceutical Statistics, 27(3), pp. 457-476. (doi: 10.1080/10543406.2017.1289949) (PMID:28281911)

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, 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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Anisimov, Dr Vladimir
• Authors: Parke, T., Marchenko, O., Anisimov, V., Ivanova, A., Jennison, C., Perevozskaya, I., and Song, G.
• College/School: College of Science and Engineering > School of Mathematics and Statistics
• Journal Name: Journal of Biopharmaceutical Statistics
• Publisher: Taylor & Francis
• ISSN: 1054-3406
• ISSN (Online): 1520-5711
• Published Online: 07 February 2017
• Copyright Holders: Copyright © 2017 Taylor and Francis
• First Published: First published in Journal of Biopharmaceutical Statistics 27(3):457-476
• Publisher Policy: Reproduced in accordance with the publisher copyright policy