In the context of clinical trials, reducing the workload of the clinical team without compromising data quality is imperative for efficiency and cost-effectiveness. One key area that demands attention is the approach to Source Data Verification (SDV) and data review. Typically, the industry relies on 100% SDV, a resource-intensive quality assurance method that often leads to higher costs and staff turnover without necessarily improving data quality. A well-known analysis by TransCelerate1 has revealed that only around 3.7% of clinical trial data changes after its initial entry, and even fewer changes occur because of data review and SDV. It begs the question: why is 100% SDV still so prevalent?
We explore two data-centric methodologies, both supported by regulatory guidelines from authoritative bodies like the FDA2 and EMA:3
- Targeted Source Data Verification (TSDV)
- Centralized Statistical Monitoring (CSM)
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For 35 years, Cytel’s scientific rigor and operational excellence have enabled biotech and pharmaceutical companies to navigate uncertainty, prove value, and make evidence-based decisions with confidence.
To celebrate this milestone, we invited Cytel’s Co-Founders, Nitin Patel and Cyrus Mehta, and CEO, Joshua Schultz, to offer their reflections on Cytel’s beginnings, its innovations in technology and statistical strategy over nearly four decades, and predictions for the future of the industry.
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In an ever-changing clinical development environment, sponsors face many challenges when designing clinical trials. These challenges can range from shifting regulations to growing pressure for a rapid time-to-market, and the variety of challenges can complicate trial design requirements. A thorough evaluation of numerous parameters is required in order to choose the best fit or optimal study, and this has led to more complex and innovative clinical trial designs.
When designing trials, biostatisticians have a choice between two main tools: commercial software or open-source development. Both options can be utilized to effectively design a trial, but which approach is best? Here, we will discuss both options and make a case for why this does not need to be an either-or question, but rather a “combined capabilities” approach that integrates open-source and commercial software.
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Health technology assessment (HTA) submissions require cost effectiveness analyses based on comparative effectiveness studies of survival benefits vs. standard-of-care options in each specific geography. Ideally, these submissions are based on large, randomized control trials (RCTs), however, most new drugs approved in the last five years, specifically in oncology and rare diseases, are being approved based on small clinical trials, often un-controlled or single arm. Often, these trials do not have overall survival as the primary efficacy parameter.
Thus, the dilemma: comparative evidence is still required for HTA submissions, but traditional investigative methods are no longer suitable to support value proposition. So where do we go from here? Read more »
On the occasion of Cytel’s 35th anniversary, co-founder Professor Cyrus Mehta discusses the founding of Cytel, the evolution of the industry over the last 35 years, and the ongoing innovations in software and statistical strategy.
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The American Statistical Association Biopharmaceutical Section, in cooperation with the FDA Statistical Association, will host its annual Regulatory-Industry Statistics Workshop in Rockville, Maryland, this year, on “Statistics Post-Pandemic: Paving the Scientific Path to Treatments, Vaccines, and Diagnostics” bringing together statisticians from industry, academia, and the FDA. Cytel experts will present the following:
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In the last 10 years, the Asia-Pacific (APAC) region has become a hotspot for clinical trials: the region contributed almost 50% of new clinical trial activity globally. The importance of APAC trials has also evolved, from the patients/sites being a contributor to address global patient enrollment issues to being a key player in global trials. This is driven by its large patient population, lower risk of competing trials, government support, pragmatic regulatory processes, lower cost of conducting trials, and strategic importance of Asian markets.
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Bayesian methods have continuously played a key role in transforming clinical research in therapeutic areas such as oncology and rare diseases, and in addressing clinical development challenges for COVID-19 drugs, devices, and biologics. From early-phase trials to late-phase development, utilizing Bayesian tools can expedite and/or de-risk trials, even when used to augment a Frequentist framework.
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People think in Bayesian terms all the time: we use prior information and the evidence at hand to make decisions in our day-to-day lives. And it is this adaptive thinking that can be so useful in clinical trials, for example, in oncology trials where the standard of care might evolve during the course of the trial. In his recent webinar, “Novel Methods of Trial Design” Prof. Yuan Ji, serving as Cytel’s executive advisor, discusses Bayesian methods in early-phase oncology trials. Let’s take a closer look. Read more »
Pharmaceutical research in oncology is increasingly focused on the development of therapies targeted at newly identified driver mutations. However, the rarity of many of these mutations presents new challenges for trial design and patient recruitment. In particular, it may be difficult in practice to recruit enough patients with the same mutation and tumor histology. One solution has been to evaluate these histology independent therapies (HITs) in basket trials, which recruit patients across multiple histologies that all share a common targetable driver mutation.
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