Which statement best describes how missing, unused, and spurious data should be handled in a GCP trial?

Which statement best describes how missing, unused, and spurious data should be handled in a GCP trial?

• A. Missing data should be ignored in analysis
• B. Data integrity checks are optional
• C. The protocol must specify procedures for accounting for missing, unused, and spurious data, including how such data will be handled and justified
• D. All data must be included regardless of quality

Answer: (C)

In a GCP trial, how missing, unused, and spurious data are handled must be planned in advance. The protocol (and the accompanying data management and statistical analysis plans) should specify exactly how such data will be identified, what methods will be used to address it (for example, predefined imputation strategies, rules for excluding data, and planned sensitivity analyses), and the justification for including or excluding data in analyses. This pre-specification ensures data integrity, transparency, and auditability, so decisions about data handling aren’t made after results are known. Ignoring missing data, treating data quality as optional, or including all data without justification can introduce bias and undermine study validity. Therefore, the protocol must describe procedures for accounting for missing, unused, and spurious data, including how such data will be handled and justified.

In a GCP trial, how missing, unused, and spurious data are handled must be planned in advance. The protocol (and the accompanying data management and statistical analysis plans) should specify exactly how such data will be identified, what methods will be used to address it (for example, predefined imputation strategies, rules for excluding data, and planned sensitivity analyses), and the justification for including or excluding data in analyses. This pre-specification ensures data integrity, transparency, and auditability, so decisions about data handling aren’t made after results are known. Ignoring missing data, treating data quality as optional, or including all data without justification can introduce bias and undermine study validity. Therefore, the protocol must describe procedures for accounting for missing, unused, and spurious data, including how such data will be handled and justified.