Treatment allocation strategies for umbrella trials in the presence of multiple biomarkers: A comparison of methods

Luke Ondijo Ouma, Michael J. Grayling, Haiyan Zheng, James Wason

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers – equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.

Original languageEnglish
Pages (from-to)990-1001
Number of pages12
JournalPharmaceutical Statistics
Volume20
Issue number6
Early online date24 Mar 2021
DOIs
Publication statusPublished - 18 Nov 2021

Bibliographical note

Funding Information:
Luke Ondijo Ouma is supported by a Newcastle University Faculty of Medical sciences PhD studentship award. James Wason is funded by the Medical Research Council (MC_UU_00002/6 and MR/N028171/1). This research made use of the Rocket High Performance Computing service at Newcastle University.
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Keywords

  • adaptive design
  • adaptive randomisation
  • constrained randomisation
  • patient allocation
  • precision medicine
  • stratified randomisation

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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