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

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 biomarkersequal 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.

Design and analysis of umbrella trials: Where do we stand?

“…They report the impact of treatment allocation strategies on operating characteristics such as statistical power, treatment allocation ratios, proportion of patients on the best treatment available to them, and accuracy and precision of treatment effect estimates. Although Ouma et al ( 53 ) showed that no treatment allocation strategy is optimal in all settings, the hierarchy and BAR approaches performed best in general. However, the hierarchy approach is heavily dependent on the validity of the pre-specified hierarchy of subgroups and performs poorest if the hierarchy is wrong.…”

“…Ouma et al ( 53 ) and Kesselmeier et al ( 54 ) have demonstrated that when overlapping subgroups are likely, an appropriate treatment allocation strategy should be pre-specified, to ensure desired trial operating characteristics are maintained. Kesselmeier et al ( 54 ) compared two approaches: (i) pragmatic allocation to the eligible subtrial with fewer patients and (ii) equal randomization.…”

“…That is, if the issue of multiple biomarkers is not addressed appropriately in the design and analysis, biased treatment effects are very likely. Ouma et al ( 53 ) investigated the performance of various approaches previously used in practice and other plausible strategies, namely, the hierarchy approach, Bayesian adaptive randomization (BAR), equal randomization, constrained randomization, and randomization with a fixed allocation probability to control. They report the impact of treatment allocation strategies on operating characteristics such as statistical power, treatment allocation ratios, proportion of patients on the best treatment available to them, and accuracy and precision of treatment effect estimates.…”

Challenges and opportunities in biomarker-driven trials: adaptive randomization

“…Fixed or adaptive enrichment designs use the biomarkers in order to restrict enrollment to the patients who are expected to get more benefit from experimental treatment than the control, which magnifies the signal and improves the power to detect the treatment effect (10)(11)(12)(13)(14)(15)(16). Basket trials (e.g., NCI MATCH, NCI MPACT) or umbrella trials (e.g., BATTLE, I-SPY 2, Lung MAP) are implemented under the master protocol based on multiple tumor types for certain genetic mutations or different genetic mutations for a single type of tumor, respectively (17)(18)(19)(20)(21)(22).…”