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Workflows to automate covariate-adaptive randomization in REDCap via data entry triggers
Jacob M Schauer1, Marc O Broxton2, Luke V Rasmussen3
1Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
Objective:
Covariate-adaptive randomization algorithms (CARAs) can reduce covariate imbalance in randomized controlled trials (RCTs), but a lack of integration into Research Electronic Data Capture (REDCap) has limited their use. We developed a software pipeline to seamlessly integrate CARAs into REDCap as part of the all2GETHER study, a 2-armed RCT concerning HIV prevention.
Materials And Methods:
Leveraging REDCap's Data Entry Trigger and a separate server, we implemented software in PHP and R to automate randomizations for all2GETHER. Randomizations were triggered by saving a specific REDCap form and were automatically communicated to unblinded study personnel.
Results:
Study arms were highly comparable, with differences across covariates characterized by Cohen's d = 0.003 for continuous variables and risk differences <2.4% for categorical/binary variables.
Conclusions:
Our pipeline proved effective at reducing covariate imbalance with minimal additional effort for study personnel.
Discussion:
This pipeline is reproducible and could be used by other RCTs that collect data via REDCap.
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