Rapid technological advancements in clinical research raise the need to employ an effective Clinical Data Management (CDM) process to ensure the production of high-quality clinical data for monitoring drug development. Clinical research is expensive in nature. It is realized that even a very small calibration bias in clinical research can have severe repercussions resulting in misdiagnosis, wrong treatment, and escalated costs associated with retesting. The avoidable retest cost has also been estimated to be in millions in the US alone. Thus, it is required that the clinical data presented is accurate, reliable, valid and reflects a true picture of the clinical research. It should possess only an arbitrarily ‘acceptable level of variation’. To meet these expectations, quality assurance (QA) is evolving as a continual and dynamic practice of identifying errors throughout a clinical study and to ensure that the study being conducted is in compliance with the regulatory standards.
QA is a systematic and independent assessment of all the clinical trial activities and records. It checks for following aspects:
- Generation, recording, analysis, and reporting of the clinical data are in accordance with the protocol, Standard Operating Procedures (SOPs) and Good Clinical Practices (GCPs).
- Identifying and correcting data processing errors, and providing feedback to data managers and research staff during and after completion of a study.
- Report any special data processing situations or deviations from coding conventions.
- Inspection of the tasks, task-execution by clinical researchers, and documents of the clinical research.
- To determine conformity of the actual conditions with the specified requirements.
- To ensure that the rights and safety of the trial subjects are protected.
- Examining the resultant clinical data for its correctness.
- To determine whether the operations performed are compliant with the federal and state environmental protection laws and regulations.
QA is a crucial activity required to be involved throughout the clinical research process to ensure high-quality and integrity of a protocol, be it in the laboratory or in the Clinical LIMS. The QA process starts with examining the patient requirements and is involved in every step until the final performance qualification wherein the test results are compared to the user requirements.
QA in CDM has numerous benefits including:
- Improves Reliability of Results: A proper QA improves the reliability of test results to enable best patient care.
- Enhance Accuracy and Consistency Through Audits: It involves the identification of procedures to be audited and careful consideration of clinical forms. An audit is a part of the QA for clinical research and should be performed from the initial data collection to the final report generation. QA of CDM process involves data entry and database audit to verify accuracy, consistency, and integrity of data. QA of statistical analysis in CDM ensures that the SAS (Statistical Analysis Systems) programs are in accordance with the SOPs and approved by an authorized personnel.
- Identifying and Troubleshooting Ambiguities: QA helps in identifying, troubleshooting, and documenting the ambiguities and inconsistencies in clinical database management activities. It also ensures a timely action for these errors to enable systemic problems to be resolved at an early stage.
- Preparation of Clinical Reports: It facilitates the preparation of SOPs and clinical reports per the regulatory guidelines. The report also includes information pertaining to any special data processing situations or deviations from coding environments. This results in a user-friendly documentation, saving time and avoiding any confusion during the study. QA reports facilitate to attain an independent, unbiased viewpoint pertaining to the timeliness, completeness, reliability, and consistency of the CDM process.
- Adherence to Compliance: It ensures management of compliance with the protocols, SOPs, and GCPs. Any violations are documented and communicated to the data manager. This procedure ensures that the design, monitoring, auditing, recording, analysis and reporting of clinical research data is managed as per the SOPs. It also ensures the efficient communication of regulatory guidelines via corporate guidelines and processes.
QA is, therefore, an indispensable part of the CDM process. Adequate controls should be implemented to ensure the integrity, authenticity, and confidentiality of data from a clinical, regulatory, and scientific perspective. QA verifies the complete clinical research process; rectifies issues on time, and improvises the processes for future trials. Taken together, QA can assure an acceptable, reliable and accurate clinical research data management process.