The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Generating high-quality clinical data is a vital but challenging task in modern drug development. Unfortunately, in the current era of ‘big data’ and global clinical operations, spanning multiple sites and digital systems, protecting the quality of clinical data has become harder than ever.
Planning your data strategy is, therefore, crucial to ensure a high-quality evidence package and increase the chances of successful clinical development. However, as we discuss in our new eBook, planning a data strategy is a complex process involving various considerations that require significant amounts of time and expertise to fully address.
Read our eBook for expert insights on planning a data strategy that can help overcome key challenges in clinical development and boost your success.
In this blog, we discuss the many data-related challenges commonly faced in clinical development and how to implement a fail-safe data strategy that can overcome these challenges, bringing effective new therapies to patients.
It is widely acknowledged among drug developers that one of their most important assets is the data generated during clinical trials. Hence, it is no surprise that many companies plan and execute a strategy to protect the quality of the clinical data they produce. It is, however, easy to underestimate just how much time and expertise you need to address the numerous and complex considerations involved in the planning process.
Unlock top tactics and tips on how to plan a rock-solid data strategy to minimize risk and boost clinical success in our latest eBook.
If you are keen to find out how to optimize your clinical data strategy, read on to discover five of the top tips outlined in our eBook from specialists working in the Strategic Consulting, Clinical Research Services, and Data Management teams. Their global reach ensures top insights from every corner of the world.
In clinical development, a high-quality evidence package is a prerequisite for a new therapy to gain approval from regulators and other key decision-makers. As such, the quality of your clinical data is one of the key factors determining whether an effective new therapy reaches patients.
Implementing a data strategy can help to protect the quality of your evidence package. However, many companies start planning their strategy quite late in the development process, which makes it difficult to address (sufficiently address) the complex considerations involved. As we explore in our new eBook, a data strategy planned well in advance of starting Phase 1 and following the industry’s best practices can help you reduce risk, expedite clinical development, and successfully achieve your business objectives.
Download the new eBook, “Are you Harnessing the Power of your Clinical Data?” to find out how to optimize your data strategy to advance clinical development.
In our previous blog, we talked about the value of planning a data strategy for the entire duration of your program (i.e., a ‘program-wide’ strategy). However, it is also important to plan for specific phases of clinical development, because they each have unique challenges. Below we discuss the major challenges commonly encountered in Phase 1 and Phase 2 studies, and the tactics you can use to resolve them. An upcoming article will engage with challenges in Phase 3 and post-market.
In the quest for clinical success, we all strive for evidence packages of the highest quality. If the clinical data is strong, then a promising new therapy is more likely to obtain approval from key stakeholders, such as regulators and payers . As a result, you’ll get the chance to develop a therapy that will help many patients (and you will likely gain returns on your investments). As we discuss in our new eBook on data strategy planning, a carefully planned data strategy can help mitigate risks to your programs and enable you to successfully achieve your goals.
Discover how to plan a data strategy that enhances your clinical programs and enables new therapies to reach patients in our new eBook.
In the high-stakes environment of clinical development, it is never too early to start protecting your valuable data assets with a first-rate strategy. So, keep reading to learn which planning approach to use, who should be involved, when it is best to start, and why it is well worth going to all the effort.
In this blog, Alla Muchnik, Senior Clinical Data Manager at Cytel, 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.
Career Perspectives: Interview with Tina Checchio, Associate Director, Quantitative Pharmacology & Pharmacometrics
QPP remains at the heart of model based drug development. Short for Quantitative Pharmacology & Pharmacometrics, it refers to several types of quantitative modeling including meta-analysis, PK/PD, statistical modeling and the modeling of go-no-go decision rules. Cytel’s expert Quantitative Pharmacology and Pharmacometrics group delivers high quality solutions to help our customers get those decisions right.
In this blog we talk to Tina who lives in Stonington, Connecticut, to find out more about her career path, current role at Cytel, and her interests outside of work.
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. Fortunately, both strategic and tactical opportunities are available to help sponsors successfully
implement a data management strategy, and ensure quality and simplicity in data collection to enable subsequent analysis. In this ebook, our experienced global data management team outlines some considerations to help sponsors navigate key decisions that need to be made throughout trial implementation.
In this blog, we share a new infographic based on this popular blog post illustrating some of the critical interactions that need to take place between data management and statistics groups to help ensure efficiency and data quality.