A multi-source adaptive platform design for testing sequential combinatorial therapeutic strategies

Alexander M. Kaizer, Brian P. Hobbs, Joseph S. Koopmeiners

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Traditional paradigms for clinical translation are challenged in settings where multiple contemporaneous therapeutic strategies have been identified as potentially beneficial. Platform trials have emerged as an approach for sequentially comparing multiple trials using a single protocol. The Ebola virus disease outbreak in West Africa represents one recent example which utilized a platform design. Specifically, the PREVAIL II master protocol sequentially tested new combinations of therapies against the concurrent, optimal standard of care (oSOC) strategy. Once a treatment demonstrated sufficient evidence of benefit, the treatment was added to the oSOC for all future comparisons (denoted as segments throughout the manuscript). In the interest of avoiding bias stemming from population drift, PREVAIL II considered only within-segment comparisons between the oSOC and novel treatments and failed to leverage data from oSOC patients in prior segments. This article describes adaptive design methodology aimed at boosting statistical power through Bayesian modeling and adaptive randomization. Specifically, the design uses multi-source exchangeability models to combine data from multiple segments and adaptive randomization to achieve information balance within a segment. When compared to the PREVAIL II design, we demonstrate that our proposed adaptive platform design improves power by as much as 51% with limited type-I error inflation. Further, the adaptive platform effectuates more balance with respect to the distribution of acquired information among study arms, with more patients randomized to experimental regimens.

Original languageEnglish (US)
Pages (from-to)1082-1094
Number of pages13
Issue number3
StatePublished - Sep 2018

Bibliographical note

Funding Information:
This research was partially funded by NIH grants P30-CA016672 and P30-CA077598.


  • Adaptive randomization
  • Ebola virus disease
  • Emerging infectious diseases
  • Multi-source smoothing
  • Platform design

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