The other side of clinical trial monitoring; assuring data quality and procedural adherence
Abstract
BackgroundData monitoring can mean different things. It can mean statistical methodologies for clinical trial monitoring, interim data analysis, monitoring for quality control or assurance or safety reporting to regulatory agencies. PurposeThe various facets of data monitoring will be discussed and reviewed from primarily an industry perspective. MethodsBy careful attention to the design and conduct of a clinical trial, the expense of monitoring can be markedly reduced. Careful attention should be given to the qualifications of investigators in the selection of clinical sites and central facilities. Site personnel must be adequately trained. The sponsor should utilize appropriately qualified individuals to supervise the overall conduct of the trial. The monitor should visit the investigator at the site of the investigation frequently enough to ensure acceptable quality. The monitor is responsible for inspecting the case report forms at regular intervals. Quality control should be applied to each stage of data handling to ensure that all data are reliable and have been processed correctly. The auditor will assess whether the site is being monitored in accordance with the monitoring plan. The determination of the extent and nature of monitoring should be based on considerations such as the objective, design and complexity of the trial. Statistical sampling may be an acceptable method for selecting the data to be verified. The monitor should ensure that adverse events are reported. Study data will be monitored on an ongoing basis to ensure patient safety. The sponsor may utilize a Data Monitoring Committee to protect the validity of a trial. ConclusionsDiscussions between industry, academia and regulatory groups regarding the optimal extent and methods for monitoring of clinical trials are encouraged.
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Standard processes are often implemented by local study staff to enhance data quality and reduce bias by detecting and reducing missing or incomplete data, inaccuracies, and excessive variability in measurements.
“Standard processes are often implemented by local study staff to enhance data quality and reduce bias by detecting and reducing the amount of missing or incomplete data, inaccuracies, and excessive variability in measurements.390”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [390]
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- Authors
- Williams, G. W.
- Affiliations
- Amgen (United States) (US)
- Corresponding author
- Williams, George W
- Venue
- Clinical Trials
- Publisher
- SAGE Publications
- Published
- 2006-12
- Volume
- 3
- Issue
- 6
- Pages
- 530-537
- ISSN
- 1740-7745 (ISSN-L)
- 1740-7753
- OpenAlex
- W2035489083
- Cited by
- 45
- References
- 15
- Citations by year
- 2026: 4 · 2025: 3 · 2023: 2 · 2022: 2 · 2021: 7 · 2020: 4 · 2018: 1 · 2016: 1 · 2015: 1 · 2014: 4 · 2013: 5 · 2012: 3
- FWCI
- 0.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Trials (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Keywords
- clinical trial monitoring, data quality, procedural adherence, data monitoring committees, investigator qualifications, site selection, quality control, adverse event reporting, patient safety, regulatory compliance
- MeSH terms
- Decision Making, Humans, Quality Control, Research Design, Randomized Controlled Trials as Topic, Adverse Drug Reaction Reporting Systems, Risk Assessment, Patient Selection, Clinical Trials Data Monitoring Committees
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