Ontology highlight
ABSTRACT: 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.
SUBMITTER: Schauer JM
PROVIDER: S-EPMC12486239 | biostudies-literature | 2025 Oct
REPOSITORIES: biostudies-literature

JAMIA open 20251001 5
<h4>Objective</h4>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.<h4>Materials and methods</h4>Leveraging REDCap's Data Entry Trigger and a separate server, we implemented software ...[more]