What Is Data Integrity in Clinical Trials and Why It Matters

Data integrity definition

Data integrity, as defined by the FDA, refers to data that is complete, consistent, and accurate.[1] They continue to set forth the idea that complete, consistent, and accurate data should follow the ALCOA principles:

  • Attributable
  • Legible
  • Contemporaneously recorded
  • Original or a true copy
  • Accurate

Data integrity also considers these attributes of data over its entire lifecycle, from collection through all handling, storage, sharing, and processing steps.

What is data integrity in healthcare?

In healthcare settings, such as hospitals, laboratories, and clinical research institutions, data integrity is concerned with patients' clinical data (i.e., medical information) being accurate and appropriately tracked through all of its movements and uses. Ensuring clinical data integrity involves rigorous checks to ensure data isn't altered, destroyed, or accessed or used in unauthorized ways, which is vital for maintaining patient privacy and confidentiality, as well as for making informed care decisions. In healthcare, patient data can originate from or be stored in diverse sources, like electronic health records (EHRs), patient registries, case report forms (CRFs), etc.

Why is data integrity important in clinical trials?

In clinical trials, data integrity is crucial since decisions about the safety and efficacy of a drug, device, or therapy under investigation are made according to the collected/processed trial data. Thus, untrustworthy or inaccurate data could lead to poor decisions being made – such as approving a harmful drug – which could have significant and wide-reaching negative impacts on patient health and safety.

Data integrity is also important for guaranteeing the scientific validity and quality of study data and results, as well as for remaining compliant with data quality and data privacy regulations such as GCP, cGMP, and HIPAA. Relatedly, strong data management practices ensure transparency and thus accountability for sponsors, helping to uphold trust toward biomedical research amongst the public.

Data integrity issues in clinical trials

Inversely, issues with data integrity can have numerous consequences, potentially impacting patient safety and well-being, the quality and reliability of trial results, regulatory compliance, trust in clinical research, or causing setbacks and delays for trial sponsors.

Several operational or design issues could compromise data integrity within clinical trials, including poor protocol design, non-compliance, inaccurate statistical analysis, lack of or poor monitoring practices, human error during data collection or entry, incomplete or missing data (lack of data validation), inadequate documentation/SOPs, equipment malfunctions, unauthorized access or tampering with data, and inconsistencies in data across study sites (the list could go on).

Any one of these issues, if unnoticed and left uncorrected, can lead to any possible combination of the potential issues mentioned above. So, this brings us to the next question…

How do you ensure data integrity in a clinical trial?

Several measures can be implemented to ensure data integrity throughout a clinical trial. Firstly, comprehensive protocols and standard operating procedures (SOPs) should be developed to guide all aspects of the trial process. Sponsors – the entity who is ultimately responsible for data integrity in a clinical trial – are responsible for ensuring that SOPs are followed rigorously, both centrally and at trial sites. This might include conducting any necessary training for study personnel involved in data collection.

Once the trial is set in motion, consistent monitoring – which could be conducted under different models, such as remote monitoring/centralized monitoring, risk-based monitoring, on-site monitoring, or a hybrid approach – is crucial for maintaining an ongoing evaluation and validation of clinical data integrity. A study monitor is usually tasked with overseeing the trial’s monitoring, and they (or a clinical research associate on their behalf) might visit study sites periodically to review documents, verify source data against case report forms (CRFs), and perform audits to help identify or correct discrepancies or potential issues. These site visits could be conducted either as a main tool of the monitoring plan, or sporadically (i.e., just when certain discrepancies identified in a remote monitoring approach require in-person investigation or resolution).

The use of electronic (so-called eClinical) systems for capturing, storing, and analyzing trial data also contributes to maintaining clinical data integrity. The sponsor organization may opt to use a single program or any combination of them, or even a custom solution, according to the specific design and goals of the trial. The systems most commonly used include EQMS, CTMS, and CDMS for overall trial management and quality control, and EDC, eCOA, and ePRO for data collection, and eCRFs for individual patient data. Most of these systems provide built-in (manual or automated) checks for inconsistencies or missing information, facilitate secure storage and backup processes, track changes made to the data, manage authorized user permission, and enable efficient query resolution between study sites and the sponsor. Although there is typically a lengthy and involved set-up time involved, the use of these systems has generally proven to be beneficial in terms of reducing human error, supporting real-time monitoring/tracking, improving data consistency and quality, and facilitating timely rectification of any queries or discrepancies that arise.

Data integrity risk assessment and data integrity checks

A thorough data management plan (DMP), usually designed by the clinical data management (CDM) team or clinical data manager, should include aspects of data integrity risk assessment. An initial risk assessment across all steps of the clinical data’s journey is important for identifying potential vulnerabilities regarding data integrity. This includes assessing the security or quality risks related to particular data collection methods, sources of errors or inconsistencies, storage and transmission of data, and unauthorized access.

Regular data integrity checks should be performed to ensure the ALCOA data integrity principles are upheld throughout the trial. A data integrity check could involve verifying the completeness and accuracy of CRFs, comparing source documents against data entered into electronic systems, conducting periodic audits of study sites, reviewing electronic system logs for any unauthorized activities or modifications, and performing quality-control checks of automated edit checks (i.e., feeding the system with erroneous data to ensure it is detecting errors).

Data integrity in global clinical trials

Data integrity is equally important in global or international clinical trials, which are becoming increasingly common. Due to cultural and language differences, divergent regulatory landscapes, and differences in local research practices, managing data integrity in international trials involves an additional layer of complexity.

Promoting clear communication and supportive collaboration among investigators from different regions can help harmonize trial operations by sharing best practices and accounting for cultural nuances that might impact participants and researchers alike. Most eClinical and data management systems accommodate multiple languages, time zones, and (to some degree) integration of regulatory standards across different jurisdictions, which can help sponsors maintain clinical data integrity in global clinical trials. For region-specific data integrity considerations and guidance, see the resources we’ve compiled below.

Further guidance on data integrity in clinical trials

See the following resources for

1. USA:

3. UK: MHRA data integrity guidance

4. EU: EMA Guideline on computerised systems and electronic data in clinical trials

Other resources for additional reading:

Conclusion

Accurate and reliable data is essential for making appropriately informed decisions about the safety and efficacy of investigational drugs or interventions. The results obtained from clinical trials influence regulatory approvals, treatment guidelines, and patient care, and thus clinical trial sponsors have an ethical responsibility to maintain high standards of data integrity.

Robust data management practices ensure the protection of participants’ rights and contribute to upholding ethical practices within clinical research. Furthermore, data integrity supports reproducibility and scientific validity in research, and following best practices for data integrity will help sponsors remain compliant with applicable legal and regulatory frameworks.

Ultimately, maintaining high levels of data integrity fosters public confidence in clinical research and helps guide the healthcare industry as a whole toward making policy and care decisions that tangibly improve patient outcomes and public health.

By conducting thorough risk assessments and data integrity checks throughout a clinical trial, researchers can mitigate potential data integrity issues and maintain the credibility of their study results, while simultaneously streamlining data processing operations and protecting participant safety.

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