Now more than ever, the clinical development path is full of make-or-break data complexities and analytical challenges. As a pioneer in evidence generation, with deep expertise in advanced analytical solutions, we are uniquely equipped to unlock the value from increasingly complex data. Life Sciences companies count on Cytel to deliver exceptional insight, minimize trial risk and accelerate the development of promising new medicines that improve human life. Cytel provides software solutions for the design and analysis of clinical trials, including industry standards East®, StatXact® and LogXact®, as well as data-focused clinical research services. With operations across North America, Europe, and India, Cytel employs 900 professionals, with strong talent in biostatistics, programming, and data management. For more information about Cytel, visit http://www.cytel.com/.
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Contact: Liz Cole
A recurring question we get from clients is whether it is worth adopting data standards such as CDISC in the early phase of their drug development, and if it is worth spending more to produce SDTM and ADaM packages at an early stage. Learn more about why this could be a good decision for your company and steps you can take towards adopting them.
In this Q&A with Laura Flight, National Institute for Health Research (NIHR) Doctoral Fellow we take a deep dive into the objectives of her recent paper "A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis" Learn more about the next steps for promoting better understanding in this area.
A specialized biopharmaceutical company had a breakthrough therapy that had the potential to be first-in-class for a rare and aggressive hematological cancer and had shown great potential in earlier clinical trial. In many breakthrough treatment areas, where the patient population is small, or there is overwhelming evidence of efficacy at Phase 2, it has become common for drugs to be approved based on a pivotal single arm trial – however, this is not always optimal. Read how synthetic control offers a practical, effective way to leverage real-world evidence and has been applied in regulatory approvals.
The right design and the right data ultimately leads to the right decisions, so obtaining fit-for-purpose data, collected based on what your protocol is looking for is vital. However, there are several data pressure points facing oncology drug developers that need specialized expertise and processes to handle. In this blog, we run through some key aspects to consider to smooth your data collection and analysis.
A client was developing a new drug for complex neurodegenerative disease in pre-clinical development. The drug may be only effective for a particular subgroup of patients. They needed to generate a hypothesis on the molecular pathway and the targeted drug activity and identify a biomarker signature defining potential response to the new drug. Read how Cytel’s analysis produced a biomarker signature that was provided to the client for in-vivo validation.
With the rise in digital technologies, there has been an explosion in volume and type of data sources we can obtain. However, new data sources bring inherent challenges to be overcome including lack of standardization, missing data, and variation in quality. Read how Cytel's data science and real-world evidence groups have helped clients apply advanced analytical techniques to large, complex historical or real-world data sets to improve their decision-making, accelerate development pathways, and enhance their probability of success.
In September 2018, FDA issued a new draft Guidance for Industry on Adaptive Designs for Clinical Trials of Drugs and Biologics. This guidance replaces the previously published 2010 draft guidance. Here, we summarize the differences between the two documents and highlight any significant new elements introduced in the most recent material. Of note, the 2018 guidance is more compact and streamlined than its 2010 predecessor, also evident by a fewer number of total pages (36 vs. 50 in the 2010 version).
Cytel Inc., the leading global provider of innovative analytical software and services to the life sciences industry, and Axio Research, a premier provider of biostatistics to pharmaceutical, biotechnology and medical device companies, today announced that they have joined forces to create the largest global biometrics organization focused on delivering advanced analytical solutions for the life sciences industry.
For the biopharma industries specifically, AI represents an opportunity to avert the R&D productivity crisis with paradigm-shifting applications such as in-silico drug design, prediction of trial risks and big data analytics. However, with every opportunity, there are risks and challenges, and this blog discusses how pharma needs to address the opacity of AI to ensure trust and credibility with all stakeholders.
This blog discusses how specialist CROs can add value and streamline processes by providing oversight of data management services delivered by another CRO. This model helps to fulfill essential regulatory obligations for biopharma companies who may lack their own internal oversight resources.
Determining appropriate stratifications and relevant clinical endpoints for specific sub-populations can be challenging. Therefore, it is necessary for development strategies to incorporate explorations and determinations of suitable biomarkers early in the development of a new therapy.
In this blog, Jonathan Pritchard, Director Business Development at Cytel, draws on his experience in commercial, clinical and technology roles within the biopharmaceutical industry and shares his insights on the primary considerations for sponsors when implementing an ePRO solution.
Across all therapeutic areas, clinical development faces well-documented, critical challenges that impact the pharmaceutical industry's ability to bring new medicines to patients – but in the oncology space, these issues are particularly acute. Read how adaptive trial designs can help address the challenges encountered in anti-cancer clinical development today by saving time, resources and improving the odds of success.
Compared to conventional approaches, a model-based approach to enrollment forecasting provides a more realistic assessment of the possible risks and outcomes for any given scenario, by accounting for the nonlinearity and randomness of real-life enrollment processes. In addition, a model-based approach offers many more advantages other than more realistic expectations.
This article will provide helpful pointers from Paul Terrill, Director of Strategic Consulting at Cytel to ensure smooth communication between statistical and clinical stakeholders.
Data is the most crucial asset in any clinical trial and is used to ultimately drive the decision-making process related to the development candidate. Therefore, for any sponsor, paying close attention to the data management aspects of clinical operations should be paramount. The principles of data management are simple and well-founded. However, the application of these principles needs careful consideration, depending on various scenarios and the size of the organization. When implementing data management for your trial, it is critical to plan ahead and fully understand all the steps and activities involved.
Data managers need to equip themselves with skills to make sense of an ever-expanding world, while maintaining adherence to core principles of safety and efficacy.
In this blog, Paul Fardy, Executive Director of Data Management at Cytel shares his thoughts on how the data manager role has evolved.
Patti Arsenault, Cytel's Global Head of Data Management, shares her thoughts on the three core elements important for the success from the data management standpoint - effective timeline management, thoughtful database design, and a proactive approach to data cleaning.
Trial sponsors must carefully plan their data consolidation and analysis strategies not only in preparation for CDISC-compliant submissions, but to respond to market influences and evolving clinical partnership models.