The management of quality clinical data collection is built on a number of core essentials- including project management, timeline management, understanding of the deliverables, alignment with statistics and selection of the right technologies. However, clinical development is a complex business and clinical data management approaches must be tailored to meet the specific needs of the trial. In this blog, we take a look at some of the key considerations to be addressed by data management across the different clinical development phases.
Phase 1 studies tend to be relatively short in duration with a small number of healthy subjects - which can mean a somewhat lower volume of clinical data for data management to handle. Often, a Phase 1 study is conducted at a single site, or taking place in a specialized Phase 1 unit . This can imply a lower level of communication complexity with fewer stakeholders than a multi-site later phase study. However, it’s important to bear in mind that the sponsor stakeholders will have a keen safety focus- so the data management team needs to adapt their communication style accordingly.
Because the Phase 1 unit will generally handle procedures at a specified time point, this may mean that data will come through to data management in sets rather than on an ongoing basis. Resourcing should be appropriately applied to handle this work flow, ensuring data is addressed as it comes in. Finally, an important aspect of Phase 1 studies is that set up may often take longer than conduct, and the protocol itself can remain fairly volatile up until First Patient In. This can make the database set up more challenging, so flexibility and responsiveness from the data management vendor is critical.
Phase 2 and Phase 3
Phase 2 and 3 studies will be robust and rigidly controlled, focusing on dose requirements and treatment effect. The studies are generally longer in duration and result in the handling of more data. In Phase 3 studies, timelines become increasingly important with sponsors often engaged in a ‘race to market’. This means that observance of timelines in data management- from database set up, through to data base lock is crucial as delays may be costly. It’s important to bear in mind that while set up time for an EDC system may delay starting, it will lead to higher quality data and swifter database lock at the end of the study.
During Phase 3, the patient experience may be incorporated into the clinical database with quality of life data and questionnaires. An ePRO system may be used which can integrate with the chosen EDC system, or increasingly this is approached as an integral part of the EDC system itself.
Since these studies will be multi-site, the data manager will have to handle a higher volume, and greater complexity of communication. Here, the sponsor as well as the clinical operations groups will be key stakeholders.
In Phase 4 studies, marketing and safety groups are key stakeholders. These studies may be following patients over a longer period of time, examining treatment effectiveness, benefit and risk.
Since these studies are observing what is happening in a real-life setting, the data collection approach needs to reflect this with a streamlined design which facilitates clean data at the point of entry. Whereas in randomized clinical trials, the sites are clinical trial ‘savvy’ and familiar not only with the clinical data collection approach, but often with the specific EDC system itself, in an observational setting the sites may only be dealing with data from a handful of subjects. Therefore ease of use of any EDC system needs to be a primary factor.
Cytel's data management group is experienced across all phases of development. The team successfully implement top notch clinical trial software packages, standards(CDASH) in case report forms and reporting(SDTM), perform efficient database builds and relevant data cleaning, and design operational support functions allowing end users to accomplish their tasks in a more streamlined manner.
Find out more about our data management services here.
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With thanks to Patti Arsenault, Director Data Management