The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
The Virtual PHUSE-EU CONNECT Conference was held from November 8 to 13 and the event was a great success, despite all of us missing the face-to-face contact.
The conference kicked-off on Sunday night with a Social Virtual event with a “Numerologist Show”. Of course this could not replace and compete with the usual “toast” we were used to do live, so we did it virtually (check out my LinkedIn post where I offer some cocktails recommendations and share the recipe of my favorite cocktail “The Negroni” with a bit of history. But please don’t do it before Friday night, you will need the weekend to recover).
Like every year, Cytel significantly contributed to the event as one of the official sponsors, running a workshop (“Predictive Analytics Using R”), chairing and co-chairing two streams (Machine Learning & Connected Health and Scripts and Macros), preparing four on demand presentations (in Application and Development, Coding and Tricks and Data Standards and Governance streams) and two posters.
In this blog, I focus on presentations related to data standards and data submission to agency, in general.
Data Monitoring Committees (DMCs) are groups of independent experts who periodically receive (by-arm) reports created by an independent Statistical Data Analysis Center (SDAC) using interim data from ongoing studies. The role of the DMC is to make recommendations about the continuation of the studies based on their best judgment and sometimes specified guidelines.
The DMC typically includes at least one statistician who votes on the decision to recommend stopping, modifying, or continuing a study. The SDAC typically is represented by at least one independent statistician and these statisticians are intermediaries between the sponsor and the DMC. The SDAC independent statisticians facilitate the efforts of the DMC by preparing and presenting summary data, taking care of meeting logistics, etc. These SDAC statisticians need ‘hard skills’ such as expertise in biostatistics, experience with clinical trial data, and knowledge of the study protocol. But it is essential that these SDAC statisticians also have the ‘soft skills’ for this role. In this blog, we highlight 10 key qualifications for these SDAC independent statisticians that are less technical, but no less essential.
When an expert statistician is paired with an experienced set of data managers, opportunities to capitalize on quantitative strategy are spotted more quickly. Statisticians can determine whether datasets can strengthen study findings by being presented in a way that uses the available data in a scientifically objective way that is at the same time in line with the clients’ strategic objectives.
The practice of combining statistical needs with the processes of data management and other related services for real world evidence, we will henceforth call RWE-Delivery. There are several models for RWE-delivery that can similarly vary with the needs of a study. Questions about process, management and timelines are just as key for this choice of delivery model, as the objectives of the delivery. Therefore, it is important to work closely with delivery teams to determine the possible needs for study completion.
The Missing Link: Risking your Traceability (and “Credibility”) when your ADaM package is not traceable back to SDTM
About three years ago, Cytel was helping a sponsor on a project where I had to conduct surveillance of some CRO deliverables, mainly for SDTM and ADaM packages. At first, I was involved in the review cycle of SDTM, and began by reviewing some initial mapping specifications including a draft SDTM Annotated CRF. The CRO in charge was quite experienced and there was nothing major to spot in all the different versions I had to review.
Surprisingly, it was not the case some months later, when I had to provide the same review support for the Biostatistics deliverables, specific to the ADaM package. The ADaM datasets overall were well designed, and there were no major open non-conformance issues. However, it was clear from the very beginning that there was something missing - a missing link between SDTM and ADaM.
Overcoming Clinical Development Challenges in Oncology with Innovative, Adaptive Designs: Complimentary Paper
Having its roots in the seminar rooms of the Dana Farber Cancer Institute, Cytel has a long record of establishing new methods in small samples, adaptive designs, Bayesian designs and multi-arm trials, to align statistical rigor to the goal of accelerating clinical development for oncology trials.
Trevor Mundel, President of the Bill & Melinda Gates Foundation, on COVID-19 and the Future of Drug Development in Emerging Economies
Trevor Mundel leads the Bill & Melinda Gates Foundation’s efforts to develop high-impact interventions against the leading causes of death and disability in developing countries. During a Cytel panel on the COVID-19 response, Trevor reflects on the complexities of data management, forecasting, dose-finding, recruitment and retention, when responding to a global pandemic.
Hear the entire conversation by clicking the button below or read further to get some of the highlights of this critical discussion.
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.