Establishing Metrics and Standardization for Non-CRF Data in EDC

Applied Clinical Trials - July 20, 2021

While Case Report Forms are a main contributor to collected data, non-CRF data such as core laboratory data and central imaging can be critical to any clinical study.

Clinical Data Management is a pivotal process in clinical research, capable of impacting the success or failure of any study. During clinical research, data is collected on protocol specifications articulated in Case Report Forms (CRFs), however, there is also a significant value addition provided by external data to the CRF, called ‘Non-CRF data’. The non-CRF data or third-party vendor data is collected through alternative channels. Thus, in collecting data from external sources, data integrity and quality have a critical influence on clinical trial data management and study success. The non-CRF data includes central and core laboratory data, central imaging (any type of medical images.), subject diaries which includes patient-oriented tools such as questionnaires pertaining to the quality of life, pharmacokinetics and pharmacodynamics data, safety laboratory data, genetic data, biomarkers, devices data and randomization data. A big part of this data is generated from services and components that are either outsourced or automated for direct patient interaction.

Process of non-CRF data collection:

While CRF is the major contributor to the collected data, non-CRF data also constitutes a significant portion, thereby contributing to safety and efficacy of the product. The non-CRF data collected during a study is specified in the clinical protocol. To generate this data, Data Transfer Agreements (DTA) are used between sponsor and vendor organizations. However, presently there are no industry-standard formats or procedures to govern this data exchange. For an efficient selection and management of vendors, a critical aspect is to review data transfer agreements for all third-party vendors.1 Hence, the DTA process is extremely critical for the quality of a clinical trial data inference. DTA enables receipt of non-CRF data from vendor to the clinical database. It also defines the structure of the database, data exchange timelines, and data definitions. This time consuming and cumbersome process is critically designed (Figure 2) and includes several challenges, limitations and intricacies requiring multiple review cycles.

The non-CRF data is not configured to EDC and is received as a separate electronic file. After the data has been transferred to a clinical database, there is often a need for manual reconciliation with the existing data. Such manual reconciliations take place between visits across CRF data, and the data stored in third party datasets using listings.

Limitations and challenges:

The process of non-CRF data reconciliation is fraught with risks of missing out on errors, missing data across datasets, identifying duplicate records etc. resulting in serious consequences on the safety outputs. These risks are compounded due to several additional challenges such as the use of standards, delivery of expected data file formats, uptake of new technology and adherence to timelines.2 For studies that rely heavily on this data, inaccuracies in non-CRF data can be dangerous and provide several underlying risks to patient safety. Despite the largely cumbersome process and careful evaluation of the vendors, there are several challenges (Table 1) in validation and reconciliation of non-CRF data. The challenges result in critical difficulties pertaining to data management, data quality, integrity, and confidentiality. In addition, third party data transfer is required to be handled by the Data Management Group (DMG) during the conduct and closeout phase of the study.

Besides the underlying data issue, there are several reasons for delays in data transfers. Common reasons for the delay include people-oriented challenges (missed data entry, data transfer efforts), technological aspect (e.g., spreadsheets are only limited to include 1 million row3), process errors (missed metadata, poor set up) etc. resulting in rejection and reiteration, consuming efforts, and contributing to delay in database locks in 50% cases.


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