Workflows to automate covariate-adaptive randomization in REDCap via data entry triggers.

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.