Randomization in the age of platform trials: unexplored challenges and some potential solutions

BMC Medical Research Methodology, vol. 25, pp. 268

Abstract

While platform trials have several benefits with their adaptive features, randomization challenges become of central relevance to the design and execution of a platform trial. This paper intends to address these challenges and explore some potential solutions. A platform type of clinical trial is a clinical trial design where multiple interventions are investigated simultaneously often against partly or fully shared controls, with new treatment arms added and completed treatment arms removed. Unequal allocation is often used in platform trials to improve statistical efficiency, deliver benefits to trial participants, and control the speed of enrollment in different treatment arms. Changes to the allocation ratio may be required after an interim analysis even when the number of treatment arms remains constant, for example, in a platform trial with response-adaptive randomization. To deliver the design efficiencies promised by the carefully optimized allocation ratio or simply to ensure a pre-determined allocation ratio, randomization methods that keep allocation proportions close to the target allocation ratio throughout randomization are helpful. Other situations commonly occurring in platform trials require special considerations for randomization methods and in some cases new classes of randomization methods. Such specific platform features include the requirement to accommodate differences in eligibility for different treatments, the need to ensure partial blinding with a 2-step randomization when mode of administration for different interventions is conspicuously different and full blinding is unfeasible, the objective to balance through dynamic randomization multiple prognostic factors or the need to accommodate limited drug supplies at the numerous trial centers, among others. The key to a successful execution of a complex randomization in the platform trial is the expert design of the Interactive Response Technology (IRT) system, where the system is built at the master protocol level and existing and potential randomization needs are incorporated from the outset. An additional, often overlooked, challenge when working with unequal allocation ratios and randomization methods to attain these, is the importance of preserving the unconditional allocation ratio at every allocation. Failure to do so might lead to a selection and evaluation bias even in double-blind trials, accidental bias, and reduced power of the re-randomization test.

Authors 13

1. Olga M. Kuznetsova

   Merck & Co., Inc., Rahway, NJ, USA (United States)

   Affiliation as printed

   Merck & Co., Inc., Rahway, NJ, USA

2. Jennifer Ross
   Affiliation as printed

   Almac Group, Souderton, PA, USA

3. Daniel Bodden Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany

4. Freda Cooner

   United States Food and Drug Administration

   Affiliation as printed

   US Food and Drug Administration, Silver Spring, MD, USA

5. Jonathan Chipman

   University of Utah

   Affiliation as printed

   University of Utah School of Medicine, Salt Lake City, UT, USA

6. Peter Jacko

   Lancaster University · Berry Consultants, LLC (United States)

   Affiliation as printed

   Berry Consultants, Abingdon, Oxfordshire, UK & Lancaster University, Lancaster, UK

7. Johannes Krisam

   Boehringer Ingelheim (Germany)

   Affiliation as printed

   Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach, Germany

8. Yuqun Luo

   United States Food and Drug Administration

   Affiliation as printed

   US Food and Drug Administration, Silver Spring, MD, USA

9. Tobias Mielke
   Affiliation as printed

   Janssen-Cilag GmbH, Neuss, Germany

10. Yevgen V. Ryeznik corresponding

    Uppsala University

    Affiliation as printed

    Department of Mathematics, Uppsala University, Uppsala, Sweden. yevgen.ryeznik@math.uu.se

    Department of Mathematics, Uppsala University, Uppsala, Sweden

11. Sofía S. Villar

    University of Cambridge · MRC Biostatistics Unit

    Affiliation as printed

    MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

12. Wenle Zhao

    Medical University of South Carolina

    Affiliation as printed

    Medical University of South Carolina, Charleston, SC, USA

13. Oleksandr Sverdlov corresponding
    Affiliation as printed

    Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. alex.sverdlov@novartis.com

    Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA