The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
A 2018 publication in the Biometrical Journal by Cytel’s Cyrus Mehta, Lingyun Liu and Sam Hsiao, ‘Optimal Promising Zone Designs’ (1) marks a new milestone for adaptive sample size re-estimation. Inspired by insights from the team's work with a number of Cytel's strategic consulting clients, it presents an easy to implement and new iteration of the popular promising zone design. The basic principle? That any investment of sample size at an interim analysis should be contingent on a minimal acceptable return on the investment. This return is expressed in terms of guaranteed conditional power, By identifying a minimum rate of return upfront, the new design offers greater efficiency to clinical trial planners. Importantly, the design concept is both easy to communicate, and easily understood among statistical and clinical stakeholders alike.
In this blog, Cytel Co-Founder and Fellow of the American Statistical Association, Cyrus Mehta shares his insights with us on the goals and key takeaways of the publication, and how it adds to the growing toolkit of intuitive adaptive designs available to drug developers today. We also share full access to the publication itself.
Cytel biostatisticians Cyrus Mehta and Lingyun Liu, together with Charles Theuer, CEO of TRACON Pharmaceuticals have recently co-authored a publication in the journal Annals of Oncology: “ An Adaptive Population Enrichment Phase 3 Trial of TRC105 and Pazopanib Versus Pazopanib Alone in Patients with Advanced Angiosarcoma (TAPPAS Trial)”. The paper explores the features of this innovative population enrichment, adaptive sample size re-estimation trial and how it overcomes some fundamental challenges of clinical development in ultra-orphan oncology indications. The publication is timely, in the context of the August 2018 news that the FDA has launched a complex and innovative designs pilot program to facilitate and advance the use of complex adaptive, Bayesian, and other novel clinical trial designs in late-stage drug development. The initiative seeks to further innovation by allowing the FDA to publicly discuss those trial designs that are being considered through the pilot program. Indeed, the TAPPAS trial incorporated regulatory input from both the FDA and EMA and received a Special Protocol Assessment from the FDA. As of the date of publication, the authors were not aware of any other pivotal population enrichment trial that has been implemented in oncology, and therefore the paper’s deconstruction of the design’s key elements will be invaluable to researchers considering similar innovative approaches.
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.
The ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop is sponsored by the ASA Biopharmaceutical Section in cooperation with the FDA Statistical Association. Each year 800 statistical practitioners come together to absorb new information on statistical practices in all areas regulated by the FDA.
Cytel was honored to be involved in the workshop program, and our subject matter experts added value to the conference by sharing their academic and regulatory experiences.
Don’t worry if you missed the event!
In this blog, we share the full slide set slide from Cytel contributions at the ASA Biopharmaceutical Section Regulatory Industry Statistics Workshop.
In a January 2017 paper (1), the FDA reviewed 22 case studies where promising Phase 2 trials did not result in efficacy, safety or both being confirmed in a Phase 3 trial.
At the outset, the authors of the paper are careful to state that the aim is not to assess why these unexpected results occurred, but rather to demonstrate how different trials contribute to developing our scientific understanding of the product.
At the recent Biosimilars Summit in Philadelphia, Cytel's Pantelis Vlachos presented on statistical challenges and flexible approaches in biosimilar development. In this blog we summarize some of the challenges and share the slides from talk.
We continue our case study series with this example of a Phase 3 design that uses Bayesian decision making combined with frequentist final analysis.
Clinical Development Background
Our biopharmaceutical client’s lead drug candidate is a late clinical-stage cancer immunotherapy for treatment of a rare oncology indication. Clinical development of therapies in this indication faces inherent challenges of patient recruitment and scarcity of data.
The sponsor had previously conducted a randomized, double-blind, placebo-controlled Phase 2 study. Moving into a confirmatory clinical trial setting, they came to Cytel for support with a trial design to address their key questions:
Adaptive sample size re-estimation designs are an important part of the statistician's toolkit. In this first in a series of East Insight videos, Cytel Statistician Charles Liu walks us through the creation of an adaptive sample size re-estimation design in East with a 5 minute demo. Watch the video and download the accompanying slidedeck to recreate the steps.
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.