Amy Crawford and the Advancement of Clinical Trials: A Focus on Adaptive Designs and Stroke Research

Amy Crawford, associated with UCLA and Berry Consultants, is contributing significantly to the evolution of clinical trial design, particularly in the context of stroke research. Her work emphasizes innovative approaches like adaptive platform trials and Bayesian methodologies to accelerate the identification of effective therapies and improve patient outcomes. This article explores her contributions to the field, highlighting specific projects and the broader impact of her work.

Amy Crawford: A Profile

Amy Crawford is not only known in the sports world as the wife of Yankees pitcher Gerrit Cole, a former college softball player and philanthropist, but also for her work in statistics and clinical trial design. She met Gerrit Cole while they were student athletes at UCLA. After getting engaged in 2015, Gerrit Cole and Amy got married in 2016. Together, they launched the Gerrit and Amy Cole Foundation, which aims to improve "the standard of living for children across the country" by focusing on pediatric cancer research, childhood hunger and education. Crawford's professional endeavors intertwine with her personal life, demonstrating a commitment to both family and societal well-being.

Adaptive Clinical Trials: A Paradigm Shift

Traditional clinical trials often employ 2-arm, randomized designs, evaluating a single intervention against a control. While effective, this approach can be resource-intensive and slow, especially in fields like stroke treatment, where options are rapidly expanding. Platform trials, on the other hand, are randomized clinical trials designed to evaluate multiple interventions simultaneously. These interventions can enter and exit the ongoing platform based on a master protocol, accelerating the investigation of multiple therapeutic options within a single infrastructure. This approach has the potential to speed up access to new interventions for stroke patients, potentially saving lives and improving outcomes.

Key Features of Platform Trials

Efficiency: Platform trials maximize the information obtained from each participant, aligning clinical research more closely with the complexities of clinical care.
Adaptability: They allow for the evaluation of multiple interventions within a single trial, with the flexibility to add or remove treatments based on interim results.
Acceleration: By addressing multiple therapeutic questions simultaneously, platform trials accelerate the identification of effective therapies.

STEP and ACT-GLOBAL: Pioneering Platform Trials in Stroke

In the realm of acute ischemic stroke, two prominent platform trials have emerged: the STEP trial (StrokeNet Thrombectomy Endovascular Platform) and ACT-GLOBAL (A Multi-Factorial, Multi-Arm, Multi-Stage, Randomised, Global Adaptive Platform Trial for Stroke). These trials leverage multifactorial designs, incorporating Bayesian modeling and other adaptive features to address multiple therapeutic questions concurrently.

STEP Trial

The STEP trial focuses on evaluating which acute stroke patients benefit from endovascular therapy (EVT). It employs a statistical methodology that moves beyond traditional clinical trials powered for a single population, aiming instead to identify specific subgroups that experience the most significant benefit. The STEP platform master protocol utilizes the NIH StrokeNet collaborative infrastructure.

ACT-GLOBAL

ACT-GLOBAL takes a broader approach, examining multiple interventions simultaneously using a multifactorial design. Both STEP and ACT-GLOBAL are designed to maximize the information obtained from each participant, to align clinical research more closely with the complexities of clinical care, and to accelerate the identification of effective therapies.

The Role of Bayesian Modeling

Bayesian modeling plays a crucial role in these adaptive platform trials. It allows for the incorporation of prior knowledge and the updating of beliefs as new data become available. This is particularly useful in identifying heterogeneous treatment responses across different patient subgroups.

Bayesian Change Point Model

The STEP trial utilizes a Bayesian change point model to address heterogeneous treatment responses across the NIH Stroke Scale. This model helps to identify the point at which the benefit of EVT changes based on stroke severity. The development of custom C code and MCMC samplers was necessary due to the limits of standard tools.

Adaptive Enrollment

Interim analyses, a key feature of Bayesian adaptive trials, direct adaptive enrollment and define actionable conclusions. These analyses allow for timely, data-driven decisions about patient subgroups, improving trial efficiency and relevance.

The "In the Interim…" Podcast Series

Amy Crawford's involvement extends to the "In the Interim…" podcast series, where she and colleagues discuss various aspects of adaptive clinical trials. In one episode, Drs. Scott Berry, Elizabeth Lorenzi, and 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 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.

Read also: The Story of Brandon Crawford

Key Topics Covered in "In the Interim…"

Statistical methodology for evaluating treatment benefits in specific patient subgroups
The fallacy of ordinal endpoints in clinical trials
The use of interim analyses for funding decisions
The application of Bayesian statistics in clinical trials
The challenges of communicating complex statistical concepts to non-statistical audiences

The Significance of Adaptive Designs in Stroke Research

Adaptive clinical trials offer several advantages over traditional trial designs in the context of stroke research.

Maximizing Information

Adaptive designs maximize the information gained from each patient by allowing for modifications to the trial based on accumulating data.

Addressing Heterogeneity

They can address the heterogeneity of stroke patients by identifying subgroups that respond differently to treatment.

Accelerating Therapy Development

By accelerating the identification of effective therapies, adaptive designs have the potential to improve outcomes for stroke patients.

Ethical Considerations

Adaptive clinical trials also raise ethical considerations, such as the potential for bias and the need for transparency. It is important to carefully consider these issues when designing and implementing adaptive trials.

Addressing the Challenges of Statistical Communication

Communicating complex statistical concepts to non-statistical audiences is a significant challenge in clinical research. The "In the Interim…" podcast series addresses this challenge by providing accessible explanations of statistical methodologies and their applications.

The Future of Clinical Trials: A Shift Towards Adaptability

The increasing popularity of adaptive clinical trials reflects a broader shift towards more flexible and efficient approaches to clinical research. As technology advances and our understanding of disease grows, adaptive designs will likely play an increasingly important role in the development of new therapies.

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