Which statement best describes data integrity across the study lifecycle?

Which statement best describes data integrity across the study lifecycle?

• A. Accuracy, consistency, and reliability of data, with ongoing data validation and audit trails.
• B. Relying solely on participant memory.
• C. Sharing raw datasets publicly without de-identification.
• D. Collecting data with no checks to speed up workflow.

Answer: (A)

Maintaining data integrity across the study lifecycle means ensuring data are accurate, consistent, and reliable from collection through analysis and reporting, with mechanisms to verify data and document how it’s handled. Ongoing data validation and audit trails are central because they catch errors early, confirm that data have not been inappropriately altered, and provide a clear record of who did what, when, and why. This traceability and continual verification preserve trustworthiness and regulatory compliance throughout the trial. Relying solely on participant memory isn’t reliable or verifiable, and data integrity rests on verifiable source data and proper documentation. Sharing raw datasets publicly without de-identification raises privacy concerns and doesn’t address data quality or traceability. Collecting data with no checks eliminates validation, likely introduces errors, and undermines the trustworthiness of the study results.

Maintaining data integrity across the study lifecycle means ensuring data are accurate, consistent, and reliable from collection through analysis and reporting, with mechanisms to verify data and document how it’s handled. Ongoing data validation and audit trails are central because they catch errors early, confirm that data have not been inappropriately altered, and provide a clear record of who did what, when, and why. This traceability and continual verification preserve trustworthiness and regulatory compliance throughout the trial.

Relying solely on participant memory isn’t reliable or verifiable, and data integrity rests on verifiable source data and proper documentation. Sharing raw datasets publicly without de-identification raises privacy concerns and doesn’t address data quality or traceability. Collecting data with no checks eliminates validation, likely introduces errors, and undermines the trustworthiness of the study results.