While Case Report Forms (CRFs) have been traditionally used to collect patient data, the use of non-CRF data is increasing in modern clinical trials. In fact, over 70% of clinical data now originates from non-CRF sources such as biomarkers, labs, and imaging.
In this blog, we look at the role of non-CRF data in clinical trials, its contribution to successful drug discovery, and best practices for handling this type of data.
What Is Non-CRF Data and Why Is It Important?
The term ‘non-CRF data’ refers to any data that is not captured using traditional CRFs or via an electronic data capture system (EDC).
This includes data obtained from:
- Wearable devices
- Patient reported outcomes (PROs)
- Biomarkers
- Labs
- Imaging
- ECOA (Electronic Clinical Outcome Assessments)
- Genetics
Each type of non-CRF data offers unique insights into different aspects of a patient’s health status in response to the treatment.
For example, genomic data offers insights into an individual’s genetic makeup, allowing researchers to understand how genetic variations may impact disease susceptibility.
Imaging data, such as MRI scans and X-rays, provide visual representations of anatomical structures and abnormalities, to help with disease diagnosis and monitoring.
Wearable devices, such as fitness trackers and smartwatches, offer real-time data on physical activity, heart rate, sleep patterns, and other health metrics, enabling continuous monitoring outside of clinical settings.
By incorporating this non-CRF data in a trial, researchers can gain a more holistic view of patients’ health, providing valuable insights for decision making around the safety and efficacy of new treatments.
CRF Data VS Non-CRF Data in Clinical Trials
While CRFs capture essential standardized data, non-CRF data provides additional context and real-world evidence that can enhance the quality and relevance of trial results.
Most clinical trials use a combination of CRF and non-CRF data. As a result, the volume and complexity of clinical trial data is increasing. In fact, the average study uses data from four or more different sources, often including data from multiple third-party vendors.
It’s challenging for researchers to handle this variety of data, and the efforts required to integrate and analyze the data can impact end-of-study timelines. Trial delays are very costly for research organizations. There are negative effects on patients too; every day without access to treatments has a huge impact on patients’ quality of life.
So how should researchers handle this increasingly complex data?
Challenges In Collecting and Managing Non-CRF Data in Clinical Trials
Collecting and managing non-CRF data presents several challenges:
- More complicated analysis: With multiple sources of external data per trial, data interoperability, integration, and analysis can be difficult.
- Robust data management needs: Integrating data from multiple sources requires data management technology to harmonize datasets and ensure data consistency.
- Stringent data protection requirements: Sponsors need to consider data security, privacy, and confidentiality when collecting non-CRF data, particularly sensitive information such as genomic data or PROs. Therefore, researchers must implement data protection measures and secure data sharing protocols to safeguard patient data.
- Data formatting inconsistencies from multiple vendors: Dealing with multiple vendors means researchers also must handle copious amounts of data in various formats and databases. It can feel impossible to achieve one, version-controlled ‘single source of truth’ for all the data.
- Validating data in different formats: The process of manually checking, validating, and reconciling data against specifications in different formats is extremely inefficient. Manually checking data and reporting issues and fixes to vendors is time-consuming, resource-heavy, and can delay submission.
One solution to these challenges is the use of comprehensive Data Transfer Specifications.
Data Transfer Specifications
Data Transfer Specifications, also known as specs or DTS, detail how non-CRF data should be collected to ensure seamless information exchange between vendors and the research organizations. The specifications are created by the data management team with input from biostatistics and programming teams, CROs and other external vendors. This should be done before data is collected to ensure it is collected exactly how you need it.
The specification itself should be compliant with CDISC and regulatory requirements, as well as in-house standards. Learn why clinical data standards are essential for both EDC and non-EDC data.
Why Are Data Transfer Specifications So Important?
Data Transfer Specifications not only define your datasets upfront and ease data collection, but also accelerate time to submission.
Essentially, you are establishing a common language for non-CRF data collection and integration, allowing you and your vendors to exchange this data seamlessly. This in turn enables easier integration of these datasets.
Without a robust DTS, the flow of information could be hindered, leading to delays, errors, and compromised data integrity.
Read about how one top 10 pharma fought the rising tide of non-CRF data.
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