Adaptive Designs for Evidence Based Oncology: Insights from the Experts

Posted by Esha Senchaudhuri

Sep 4, 2014 1:10:00 PM

Imagine if we were to count the number of possible reasons that investigators might have for monitoring a biomarker during a clinical trial, and multiply that number by the number of possible adaptive designs available for such investigation. We would naturally assume that whatever the number, it would be rather large. This poses an interesting question for a sponsor of an adaptive clinical trial. Are there any general principles for trial design that may be gleaned from these various possible scenarios?

A recent article in the American Journal of Managed Care confronts a range of issues in developing adaptive clinical trials for oncology. While providing a broad overview of the practicalities of using such designs, it also invites distinguished statisticians from across the pharmaceutical industry to offer collective insights based on their experiences designing several hundreds of unique adaptive trials.

Cytel Consulting’s Jim Bolognese was invited to partake in the discussion. He provides these important observations for trial designers and sponsors: 

Observations on Program Development and Trial Design:

“To evaluate the use or material advantage of an adaptive design over traditional design creates upfront work—more time in advance planning, increased use of resources (including recruiting statisticians and clinicians to help with the design), and increased expenditure. All potential adaptations need to be predefined and the statistical performance characteristics of the adaptive design, if chosen, need to be documented. The goal is to more than offset this increased upfront cost with greater later cost savings." 

Jim Bolognese co-authored a white paper with Cytel CEO Ranganath Nayak, providing guidance on how to determine whether an adaptive design will likely be cost-effective. In it they outline ten steps to determining whether to pursue an adaptive program, based on factors ranging from expected enrollment and drug supply, to the availability of statistical expertise and helpful software. 

Observations on patient recruitment:

“Two key criteria that need to be considered when applying an adaptive design are the expected recruitment rate of patients and the time after treatment when a primary end point (like a good biomarker) can be observed. For example, if the end point is mortality at 1 year after treatment, but if recruitment stops at 6 months, then there’s a need to identify a biomarker as a surrogate readout at an earlier time point, such as 2 months.”

In general, recruitment can be perilous territory for any clinical trial. Oftentimes, simulations prove quite beneficial when aiming to forecast recruitment and enrollment milestones accurately. 

Observations on regulation:

According to Jim, regulatory agencies act more favorably towards Phase 1 and 2 adaptive trials, than Phase 3 trials. Therefore, one way to improve Phase 3 adaptive trial success is to follow the FDA and EMA guidelines which encourage the use of independent DMCs for trial monitoring and interim decision-making. Jim notes, “There could always be a perception of a potential for bias without a DMC external to the study sponsor. So regulatory agencies want that the sponsors be blinded to the results of interim analysis. Unblinded information is made available to the DMCs, who then make their recommendations.” 

Related Items of Interest

Cytel Whitepaper on Adaptive Design

To Adapt or Not to Adapt? 10 Steps to Deciding Whether an Adaptive Trial is For You

Reducing Risk and Improving Efficacy of Clinical Trials: the Adaptive Design 

 

Topics: Access to Paper PDF, Dose-Escalation, Cytel Strategic Consulting, Adaptive Clinical Trials

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