Data extraction error in pharmaceutical versus non-pharmaceutical interventions for evidence synthesis: Study protocol for a crossover trial

Abstract

Background: Data extraction is the foundation for research synthesis evidence, while data extraction errors frequently occur in the literature. An interesting phenomenon was observed that data extraction error tend to be more common in trials of pharmaceutical interventions compared to non-pharmaceutical ones. The elucidation of which would have implications for guidelines, practice, and policy. Methods and analyses: We propose a crossover, multicenter, investigator-blinded trial to elucidate the potential variants on the data extraction error rates. Eligible 90 participants would be 2nd year or above post-graduate students (e.g., masters, doctoral program). Participants will be randomized to one of the two groups to complete pre-defined data extraction tasks: 1) group A will contain 10 randomized controlled trials (RCTs) of pharmaceutical interventions; 2) group B will contain 10 RCTs of non-pharmaceutical interventions. Participants who finish the data extraction would then be assigned to the alternative group for another round of data extraction after a 30 min washout period. Finally, those participants assigned to A or B group will be further 1:1 randomly matched based on a random-sequenced number for the double-checking process on the extracted data. The primary outcome will be the data extract error rates of the pharmaceutical intervention group and non-pharmaceutical group, before the double-checking process, in terms of the cell level, study level, and participant level. The secondary outcome will be the data error rates of the pharmaceutical intervention group and non-pharmaceutical group after the double-checking process, again, in terms of the cell level, study level, and participant level. A generalized linear mixed effects model (based on the above three levels) will be used to estimate the potential differences in the error rates, with a log link function for binomial data. Subgroup analyses will account for the experience of individuals on systematic reviews and the time used for the data extraction. Discussion: This trial will provide useful evidence for further systematic review of data extraction practices, improved data extraction strategies, and better guidelines. Trial registration: Chinese Clinical Trial Register Center (Identifier: ChiCTR2200062206). © 2023