The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
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.
In the 2010 draft FDA ‘Guidance for Industry on Adaptive Design Clinical Trials for Drugs and Biologics', the agency makes an important distinction between ‘well understood’ and ‘less well understood’ adaptive designs.
‘Well understood” adaptive designs may include such approaches as adaptation of eligibility criteria, adaptation for stopping early and adaptations to maintain study power based on blinded interim analyses of aggregate data. For these 'well-understood designs', there is little concern from the FDA about their potential to be implemented in adequate and well-controlled trials. On the other hand, at the time of the drafting of the guidance at least, ‘ less well understood designs' (which include such approaches as adaptations for dose selection studies, adaptation of patient population based on treatment-effect estimates, and adaptation for end-point selection based on interim estimates of treatment effect) gave greater concern. Interestingly, the FDA Adaptive Designs for Medical Device Clinical Studies : Guidance for Industry and Food and Drug Administration Staff does not adopt this distinction.
A recent article, Addressing Challenges and Opportunities of “Less Well-Understood” Adaptive Designs (He et al 2016) (1) takes a look at some of the perceived challenges of these designs and ways in which they may be overcome. The publication is the result of work by a best practice sub-team formed by the DIA Adaptive Design Scientific Working group in January 2014. Cytel's Yannis Jemiai is a member of this group, and one of the co-authors of the article.
In this blog, we take a look at a few of the challenges outlined and some of the suggested mitigations. One aspect covered in the publication is seamless designs- and given the scope we'll devote a separate blog to this area.
At the recent JSM in Chicago, Cytel’s Sam Hsaio and Lingyun Liu alongside Genentech's Romeo Maciuca, presented a framework for inference in adaptive bioequivalence trials with unblinded sample size re-estimation.
In bioequivalence trials where the variance is often unknown, and the sample size small, using boundaries derived under the assumption of a normally distributed test statistic may lead to type I error inflation. This problem can be overcome with p-value combination methods, however these approaches generally do not directly provide confidence intervals for the geometric mean ratio on the scale of the original pharmacokinetic endpoint.
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.
Francois Beckers, Global Head of Biostatistics & Epidemiology at Merck KGaA joined us at the East User Group Meeting in March and presented case studies of Merck KGaA’s experiences with Blinded Sample Size Re-estimation in early phase studies, more specifically in the context of biosimilar studies.
When designing clinical trials, many trial designers are advised to keep the trial simple. Prima facie, the keep it simple principle seems like sound advice. There are various logistical uncertainties that arise when implementing a clinical trial, and the more simple a trial – so conventional wisdom says – the easier it is to respond to these uncertainties.
According to Zoran Antonijevic, a Senior Director at Cytel Consulting, there is reason to doubt such conventional wisdom. After all, flexibility is hardly a virtue of a traditional trial design. Simple designs may seem to make it easier to monitor data and report results. However, a flexible design can better address remaining uncertainties in product development. These uncertainties are related to treatment effect, dose selection, or a sub-population that would experience the best benefit/risk from the treatment.
The VALOR trial recently applied a promising zone design to a Phase 3 evaluation of Vosaroxin, a candidate for the treatment of relapsed/refractory acute myeloid leukemia. CMO Dr. Adam Craig reports that there were intial anxieties about financing a trial that required 800 patients. However, a promising interim look raised investors' confidence in the treatment's success, leading to a high-powered trial of 712 patients.
To view a YouTube video of Dr. Adam Craig talking about the Phase 3 VALOR trial, click below.
A new JAMA study on discontinued randomized trials in Switzerland, Germany and Canada, reports that poor recruitment accounts for 101 out of 253 trials that were eventually discontinued (or about 10% of the 1017 trials which participated in the study). When restricted to industry-sponsored trials with non-healthy volunteers, poor recruitment accounted for the discontinuation of 40 trials out of 119 that were discontinued. Across the board, poor recruitment was the foremost cause of trial discontinuity.