The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Can I submit software programs other than SAS? What software programs should I submit? Are sponsors required to submit executable programs?
Do I need to rename my software programs so that they all have the same extension e.g. “.txt”?
Can I make use of macros in my software programs and if so, should macros be part of the submission package?
What kind of documentations for software programs should I include in the submission package?
Do I need to follow any particular style and conventions when writing software programs that will be part of a submission package?
A single topic generates so many questions! Get the answers in this blog.
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.
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.