Cytel Blog

The combination of greater access to electronic health records, bigger electronic claims datasets, and the need for more clinical insight in ensuring patient safety, has made observational studies an important new tool in trial design. Observational studies typically take non-randomized data from outside of a trial and use quantitative and modeling techniques to draw conclusions from big datasets. While typically used for HEOR and market access, augmenting regulatory submissions with observational studies is gaining prominence. As with all data analyses, there is an implicit rule of ‘garbage in-garbage out,’ where data that is not up to the standard required for the formation of sound scientific judgment, should not be used. Sponsors should rely on the most sophisticated tools and advanced analytics to make the most rigorous use of available data.

With the rise in digital technologies, there has been an explosion in the volume and type of data sources. We can obtain information about individual health from social media data and mobile apps, to wearable sensors and electronic health records. Corporations and governments even use insurance claims data as sources of data for analyses.

This data could yield a more robust and complete picture of diseases, the patient journey, and the effectiveness of interventions in the real world. This in turn is often used by life sciences leaders to make better drug development, reimbursement, and clinical decisions. However, apart from accessing and curating this data, we also need to harness advanced analytical techniques to generate evidence, including the sophisticated use of statistical methods. The RWE data sciences team therefore must be chosen carefully to take on the challenges of these novel uses of data.

The delivery of RWE-analyses requires more than simply statistical knowledge. The variety of RWE methodologies reflect the range of opportunities sponsors have, to cast their assets in the best light. In this blog we outline the RWE design and staffing needs of a specific kind of observational study, namely natural history studies, as regulators are increasing demand for these explanatory assessments of the biochemistry of disease progression.

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 clinical development, data is the vital ‘foundation’ that supports your programs. To successfully bring a promising new therapy to patients, the quality of your evidence must be strong enough to gain approval from key decision-makers, including regulators, payers, and health technology assessment (HTA) agencies.

So, how can you strengthen the quality of your clinical evidence package? A key solution is to optimize your data strategy. As we discuss in our new eBook on data strategy planning, making just a few small changes to your planning approach can strengthen your ‘foundation’ and generate various benefits that enhance and expedite clinical development.

Download our free eBook to find out how to optimize your data strategy to boost success in clinical development.

Cytel Inc. and Axio Research joined forces in June 2019, expanding our ability to solve the most complex analytical challenges for the life sciences industry. Cytel offers a full range of clinical data management services delivered by advanced analytics experts with global reach.

In this blog, we talk to Ronald Dumpit, who is based in Bremerton, Washington to find out more about his career path, current role at Axio and his interests outside of work.

PhUSE EU Connect 2019 was held in the beautiful city of Amsterdam between the 10th and 13th of November. This clinical data science conference comprised 19 Streams, including 150 papers, 24 posters and 3 engaging data scientists as keynote speakers. The event was well attended and had several interesting and innovative presentations. Caroline Terrill, Associate Director of Statistical Programming at Cytel UK, conducted a session “No Place Like Home: Managing Remote Programmers Remotely” and stood out as the winner in the Personnel Management category. Based on 5 years' experience of managing remote programmers, Caroline’s paper gives guidance on issues to be considered and traps to be avoided if you are managing people who work remotely.

In this blog, we share the presentations from our Statistical Programmers and summarize some of the sessions that our team members attended.