The degree to which data are complete, consistent, accurate, trustworthy, and reliable throughout the data lifecycle.
Data integrity represents the fundamental requirement that clinical trial data accurately and completely represent the observations made during the trial and remain unaltered except through authorized, documented processes. The concept encompasses not merely the absence of errors but the assurance that data are attributable, legible, contemporaneous, original, and accurate throughout the entire data lifecycle from initial recording through analysis and archival.
The ALCOA principles provide a practical framework for evaluating data integrity. Attributable means that data can be traced to the person who generated it. Legible means that data can be read and understood. Contemporaneous means that data are recorded at the time the observation is made. Original means that data are the first recording (or a certified copy). Accurate means that data correctly represent the observation made. Some frameworks extend these principles to ALCOA+ by adding complete, consistent, enduring, and available.
Maintaining data integrity requires both procedural and technical controls. Procedural controls include training, supervision, documentation practices, and audit trails. Technical controls include system validation, access restrictions, backup procedures, and electronic signature requirements. The increasing use of electronic systems in clinical trials has introduced new considerations for data integrity, including requirements for system validation, audit trail functionality, and secure data transmission. Regardless of whether data are captured electronically or on paper, the sponsor and investigator share responsibility for ensuring that trial data are reliable and suitable for supporting regulatory decisions.
Electronic systems
"The electronic data capture system maintained a complete audit trail documenting every data entry and modification, including the user identity, timestamp, original value, and new value, supporting data integrity requirements."
Document control
"When the original laboratory report was found to contain an error, the site obtained a corrected report from the laboratory rather than altering the original document, maintaining data integrity through proper source document management."
A CDISC standard that defines the structure and content of analysis-ready datasets derived from SDTM data, supporting efficient generation of statistical analyses and displays for regulatory submissions.
A secure, computer-generated, time-stamped electronic record that automatically captures the creation, modification, or deletion of data, including the identity of the operator and the date and time of the action.
An international nonprofit organization that develops and supports global data standards for clinical research, enabling consistent and efficient exchange of clinical trial information.
The process of detecting, correcting, and resolving inaccurate, incomplete, or inconsistent data in the clinical trial database to ensure data quality and reliability for analysis.
The formal process of making the clinical trial database unmodifiable once all data have been entered, reviewed, cleaned, and verified, marking the transition from data collection to statistical analysis.
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