Clinical Data Oversight Webinar

Why is Clinical Data Oversight Important?

Oversight is a requirement of the ICH E6(R2) addendum, according to which the ultimate responsibility for the quality and integrity of the trial data resides with the sponsor. What is more, this addendum also requires a risk-based approach to quality management and oversight. Conducting clinical data oversight in compliance with the ICH E6(R2) addendum can be challenging, particularly for companies that lack functional experts internally.

There are several ways to mitigate risks with an outsourcing model, including:

  • Ensuring biometrics documents are effective
  • Ensuring the right tools are available and used correctly
  • Ensuring biometrics oversight by experienced personnel

On October 27, CROS NT hosted a complimentary webinar on clinical trial data oversight. During this webinar we reviewed the components of biometrics oversight and suggested an effective data oversight strategy.

Continue reading to learn about five key elements of effective data oversight strategy.

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Clinical Trial Data Oversight Strategy – Key Elements

At CROS NT, we have developed a unique approach to clinical trial data oversight, built on our work with clients to optimise processes and maximise confidence in data quality. Our Biometrics Optimisation Solution (B.O.S.) is an end-to-end solution for biometrics management, implementation, and oversight, providing targeted resourcing and supporting eClinical system selection and deployment.

The B.O.S. solution includes 5 key elements that in our experience are instrumental in planning an effective oversight strategy regardless of the model, as they provide a systematic approach to understanding your current approach and designing and implementing an oversight method that meets your needs now and adapts as needs change.Clinical Data Oversight elements

Biometrics Consulting and GAP Analysis

Before implementing an oversight solution, it is critical to understand your current systems, processes and infrastructure, along with your current strategy for handling clinical trial data, even if this strategy is to outsource data activities to the clinical vendor of choice. This is where the GAP Analysis plays a critical role.

Through a targeted assessment, an understanding of where data is now, and how it is handled, along with your future plans can provide a roadmap to where you want to be. Do you want to eventually build a biometrics department? Do you have dispersed data that you need to standardise? Do you need a better way to review biometrics documents? The GAP Analysis findings will determine what steps to take to plan and implement your optimal data oversight strategy.

Process Development

Once you have identified the needs, processes can be defined and implemented. One approach is to deploy a team of data experts who work alongside you to assist with immediate vendor oversight and review, thus ensuring adequate risk management and control. For example, if you are receiving Statistical Analysis Plans from your vendor, do you know how to review them appropriately? Do you have a resource who can ensure the methodology is appropriate? This is something that can be addressed by a deployed team.

Process development can also mean the more traditional SOP and Work Instruction development. For example, do you have an SOP on protocol development? Does it adequately and appropriately address statistical input and review? A specialised team can assist with the creation or revision of SOPs as needed, supporting either a continued outsourcing strategy or an eventual establishment of an internal biometrics team.

System Selection and Deployment

Understanding what, if any, systems you need to standardise or deploy is crucial. An example to consider is the need to have a consistent EDC system versus having a set of features that are critical, regardless of the system the vendor deploys. If your data collection includes PRO data, it could be time to consider an ePRO strategy. This comes with its own set of requirements and considerations and the right consultant can help you navigate the myriad of options and decision.

We also advise to consider the need to standardise (integrate) dispersed data sources and evaluate tools to view them in an aggregated manner.

Centralised Biometrics

Centralised biometrics is an outsourcing model that highlights data as an asset and keeps all data activities with one specialised vendor. This model can create considerable costs savings through standardisation and improved efficiency and yield a streamlined approach to oversight and faster integration to support regulatory filings. We recommend considering a centralised approach, based on the status of and plans for development programs.


The last element of an oversight strategy is governance and a complete oversight solution should be capped off with governance. The establishment of an overall governance plan, metrics and KPIs along with a targeted risk management plan, will create the documentation you need to prove effective oversight of your clinical trial data and fulfill the requirements of ICH E6 (R2).

Ensuring Effective Clinical Data Oversight

Regulatory authorities and international standards are making data quality and integrity a priority with the Sponsor ultimately responsible for compliance.

The ICH GCP E6(R2) addendum is the most important update to the ICH guidelines in 20 years with the goal of:

  • Having Sponsors adopt a risk-based approach in design and execution of clinical trials
  • Increase safety for patients by increasing vigilance of potential misconduct and ensuring better data quality and more efficient monitoring
  • Not allowing Sponsors to also outsource the responsibility for quality and integrity of data collected from sites

Regardless of the type of external consultant that supports you with your clinical trial data oversight, a correct and effective data oversight strategy allows you to not only ensure compliance with the ICH GCP E6(R2) addendum, but also increases your confidence in your clinical trial results and supports the progress of development programs.

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