Evolving Variants, Shifting Outcomes: Implications for Nonconcurrent Controls in Platform Trials for COVID-19

The COVID-19 pandemic spurred the adoption of platform trials for efficient drug development. These trials evaluated multiple treatments simultaneously but faced challenges due to temporal shifts in patient characteristics, trial conduct, and standard of care. A key concern was the use of nonconcurrent control (NCC) data, which can enhance statistical power but may introduce bias if the effects of temporal shifts are not properly adjusted. In this study, we empirically evaluated temporal shifts in clinical outcomes (i.e., time to recovery through Day 28, clinical status on Day 14, and mortality through Day 28) using real-world platform trial data from the ACTIV-1 Immune Modulator trial, accounting for the evolving landscape of SARS-CoV-2 variants in the CoV-Spectrum database. We have investigated the operating characteristics of the existing methods using concurrent control and NCC data based on a resampling-based simulation study using ACTIV-1 IM trial data, as well as simulations under hypothetical variant-driven temporal-shift scenarios that explicitly modeled changes in circulating SARS-CoV-2 variants. Our empirical analyses revealed that assumptions in simulation studies-such as specific patterns of temporal shifts and constant treatment effects over time-might not hold in real-world settings. Simulation studies further showed that in such complex settings, even adjustment methods for leveraging NCC could introduce an unacceptably large bias. Our findings provided an empirical foundation for improving the integration of NCC data and ensuring reliability and efficiency. Real-world evidence underscored the need for robust statistical methods that account for the temporal shifts in platform trials.