The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
On August 29th 2018, the FDA announced (1) that it would be establishing a Complex Innovative Trial Design (CID) Pilot Meeting Program. This follows the release earlier in August of a draft guidance (2) to help advance effective and innovative clinical trial designs early in drug development that can expedite new cancer therapies.
A paper "Best practices case studies for 'less well-understood' Adaptive designs", has been published by the DIA Scientific Working Group on Adaptive Designs as a twin document to the previously discussed "Challenges and Opportunities of 'Less Well Understood' Adaptive Designs". This publication furthers understanding by reviewing 10 important case studies and sharing details on their design and operational characteristics, as well as related regulatory interactions.
To read an abstract and details of the full publication click here.
In this blog we'll take a look at some of the case studies under discussion.
Our client, an emerging biotechnology company, was preparing for the next stage of development for their novel compound in a rare disease. They had two major concerns which they wanted the clinical trial design to address- an anticipated difficulty in recruiting subjects to the trial, and the cost and time investment associated with running separate phase 2 and phase 3 trials. They approached Cytel’s strategic consulting team for an innovative solution.
An inferentially seamless Phase 2/ 3 design with promising zone was proposed as a means to address the sponsor’s objectives. Because of uncertainty regarding which dose would be selected and what the effect size of the selected dose would be, the team proposed design options which allowed for adjustment of the sample size using information learned at the interim analysis. Several seamless phase 2/3 designs, with and without adaptive sample size re-estimation were evaluated through simulations using East 6.4.
The simulations evaluated various design parameters such as maximal sample size, timing of the interim analysis, size of the promising zone, and efficacy and futility boundaries. Designs were compared on the basis of overall power, average sample size, conditional power, probability of entering each interim zone, and number of overruns.
The inferentially seamless design has the potential to accelerate clinical development by removing the ‘white space’ between phases 2 and 3. Where the sample size is increased adaptively at the interim analysis by a specified percentage of the original pre-planned sample size, an overall increase in power could also be achieved. The sample size re-estimation design provided a boost to power where the interim results fell in the promising zone. The client benefited from a design which only calls for additional investment of patients and resources when this investment would meaningfully boost the chances of success.
Cytel's statistical consulting team help you decide if an adaptive approach is right for your trial. Read further examples of our work by clicking below.
In order for adaptive designs to reach their potential, it’s critical that knowledge is effectively dissemirnated within the medical research community – in particular detailed information about the operating and statistical characteristics of specific designs and insights as to their benefits and limitations.
Cytel recently announced the publication of an important article in the New England Journal of Medicine which takes a leap forward in promoting better understanding of adaptive designs particularly in a confirmatory setting. We'll discuss some of the highlights of the article in this blog.