24: STEP Statistical Modeling

In this episode of "In the Interim…", Dr. Scott Berry, Dr. Elizabeth Lorenzi, and Dr. Amy Crawford discuss the STEP platform trial’s statistical methodology for evaluating which acute stroke patients benefit and which do not from endovascular therapy (EVT). The discussion critiques the inadequacy of traditional clinical trials powered for a single population to show benefit, as the goal of the trial is to identify who benefits, not if the entire population has a net benefit. The team walks through the development and simulation of a Bayesian change point model, addressing heterogeneous treatment responses across the NIH Stroke Scale. The adaptive platform design leverages scheduled interim analyses to draw timely, data-driven conclusions about patient subgroups, improving trial efficiency and relevance. The episode also previews scaling to two-dimensional modeling, incorporating both stroke severity and time since last known well, and emphasizes ongoing clinical trial simulation and close integration between clinicians and statisticians throughout trial design and execution.

Key Highlights

● STEP platform master protocol and the NIH StrokeNet collaborative infrastructure

● Clinical rationale for Bayesian change point modeling of the effect of EVT across the patients

● Shift from single to dual change point models to reflect regions of equivalence

● Development of custom C code and MCMC samplers due to limits of standard tools

● Interim analyses direct adaptive enrollment and define actionable conclusions

● Future extensions to multidimensional change point curves modeling