Stratified randomization for platform trials with differing experimental arm eligibility

Platform trials facilitate efficient use of resources by comparing multiple experimental agents to a common standard of care arm. They can accommodate a changing scientific paradigm within a single trial protocol by adding or dropping experimental arms-critical features for trials in rapidly developing disease areas such as COVID-19 or cancer therapeutics. However, in these trials, efficacy and safety issues may render certain participant subgroups ineligible to some experimental arms, and methods for stratified randomization do not readily apply to this setting.

We propose extensions for conventional methods of stratified randomization for platform trials whose experimental arms may differ in eligibility criteria. These methods balance on a prespecified set of stratification variables observable prior to arm assignment that remains the same across experimental arms. To do so, we suggest modifying block randomization by including experimental arm eligibility as a stratifying variable, and we suggest modifying the imbalance score calculation in dynamic balancing by performing pairwise comparisons between each eligible experimental arm and standard of care arm participants eligible to that experimental arm.

We provide worked examples to illustrate the proposed extensions. In addition, we also provide a formula to quantify the relative efficiency loss of platform trials with varying eligibility compared with trials with non-varying eligibility as measured by the size of the common standard of care arm.

This article presents important extensions to conventional methods for stratified randomization in order to facilitate the implementation of platform trials with differing experimental arm eligibility.