The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Interview with Kannan Natarajan: Drug Development in Rare Diseases - Need for Innovation in Statistical Thinking
Cytel is delighted to have Kannan Natarajan speaking at the “Complex Innovative Trial Design Symposium and East User Training” on November 6 in Boston, MA. We got a chance to sit down with Kannan and talk about his career in statistics, the changing role of statisticians, his views on evolving statistical thinking, estimands and relevance of technology in the context of rare diseases.
October 3, 2019 was an important day for the ADaM team as it marked the release of the ADaM Implementation Guidance (Ig) 1.2. Download the new guidance from the CDISC website here.
In 2018, several previews of the new Ig were made at CDISC Interchanges all around the world and at PhUSE conferences, it was only a few weeks ago that the final version was released. At first glance, the new Ig does not seem to contain a lot of new concepts and ideas. However, a critical and in-depth review clearly shows the efforts of the entire team to release this new Ig. Creating a new standard or releasing a new version of an existing standard is not an easy job. You need to ensure that you do not introduce anything that contradicts any of the existing CDISC standards. This includes, not only new variables but also any changes in existing sentences or adding entirely new sections that may cause misinterpretations or discrepancies. Moreover, every standard team member comes with their own background, company needs, and specific indication needs. It is not always easy to propose a solution that can satisfy everyone on the team.
A disease is generally considered to be rare if it affects one patient per 200,000 people (1) and most rare diseases affect far fewer than this. However, collectively rare diseases are relatively common, affecting 350 million patients worldwide (2). The path to diagnosis for these patients is often a long, difficult battle and even once the diagnosis is made, it is likely there will be no suitable treatment available. For 90% of rare diseases, there is no approved therapy (2). There is, therefore, a pressing need to develop new, effective therapies that can bring hope to rare disease patients. However, the clinical development environment for life-threatening, rare diseases is fraught with challenges. By their very nature, rare indications have few patients and limited sample size. This scarcity of patients also results in a lack of available information and knowledge about the disease from the best endpoints, to the treatment effect size or the variability of response between subgroups.
"If you went to bed last night as an industrial company, you're going to wake up today as a software and analytics company," CEO of GE, Jeff Immelt
We are living in a new digital world which is evolving every day. Both personally and professionally, we rely on technology for many of our routine activities, and examples of digitization are prevalent across industries. Retail is a big example of how several chains have moved from physical stores to creating an online presence. In some years from now, people perhaps won’t have to learn driving as more self-driven cars will hit the road. Healthcare does not fall far behind in this race towards digitization.
In this blog, we will examine some of the different ways that digitization is set to shift the drug development paradigm.
In place of collecting data from patients recruited for a trial who have been assigned to the control or standard-of-care arm, an external control creates a comparator arm using either real-world data-sets such as electronic health records or previous clinical trials. The external control offers a practical, effective way to leverage real-world evidence and has been applied in regulatory approvals. In this blog, we share an illustrative example of how we can help customers in this emerging area of interest.
This article was originally published as part of a series by pharmaphorum in association with Cytel and is reproduced with their permission.
Ever felt like you ended up somewhere unexpected, not quite knowing what path you took to get there? This is the situation pharma companies can face without the right input from statisticians on clinical trial design – and the results can be very costly. As part of a series of articles in association with Cytel, pharmaphorum spoke with Natasa Rajicic about the vital role statisticians play in improving trial design and preventing expensive mistakes and trial failures.
In this blog, from our career perspectives series, we talk with Jayshree Garade Associate Director, Statistical Programming about her truly global career path at Cytel. Jayshree completed her Masters degree in 2006 and began her professional career with Cytel in Pune India. In the years to follow she supported high profile trials for a number of sponsors, before transferring to our offices in Massachusetts USA. She is currently leading a programming team, working remotely from her North Carolina home. Read on to learn more about life and opportunities at Cytel.
The term biomarker signature describes the behavior of a set of biomarkers that define a signature to maximize the prediction performance. We examine the behavior of specific biomarkers as a set that consistently fluctuate together to maximize the accuracy on predicting the disease-related outcome.
How we apply a biomarker signature depends on the prediction problem. A prognostic biomarker signature is used to predict the disease progression, a risk biomarker signature is used to identify sets of subjects that are likely to develop a disease, and a predictive biomarker signature is used to determine the patients that are likely to respond to a particular treatment. Predictive biomarker signatures are used often in oncology to stratify patients with a specific cancer into sub-populations and develop targeted therapies for the diseased population subtypes defined by the biomarker signature.
In this blog, we share an example project that our data science team has worked on supporting this work. The case study forms part of a new ebook 'Innovative Data Science and Real-World Analytics Approaches in Practice' and we are also delighted to provide the link for download as part of the article.
Health economics and adaptive design methods share common ground in that they both aim to support more efficient and accurate decision making that can enable faster patient access to new health technologies. However, to date, there has been a limited understanding of how, if at all, the two approaches are being used together.
A paper, “A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis,” exploring this important topic was published in the April 2019 issue of Value in Health (1) . In this blog, we catch up with Laura Flight, National Institute for Health Research (NIHR) Doctoral Fellow and the primary author of the paper for a deep dive into the objectives of the publication, key findings and the next steps for promoting better understanding in this area.