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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Workflows to automate covariate-adaptive randomization in REDCap via data entry triggers.

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Covariate-adaptive randomization algorithms (CARAs) integrated into REDCap reduce imbalance in clinical trials. This software pipeline enhances randomization efficiency and reproducibility for researchers.

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Area of Science:

  • Clinical Trials Methodology
  • Health Informatics
  • Biostatistics

Background:

  • Covariate-adaptive randomization algorithms (CARAs) are effective in minimizing covariate imbalance in randomized controlled trials (RCTs).
  • Limited integration of CARAs into electronic data capture systems like REDCap has hindered their widespread adoption.
  • The all2GETHER study, an RCT focused on HIV prevention, sought to address this integration gap.

Purpose of the Study:

  • To develop and implement a software pipeline for seamless integration of CARAs into REDCap.
  • To automate the randomization process within the REDCap environment.
  • To facilitate the use of advanced randomization techniques in clinical research.

Main Methods:

  • A software pipeline was developed using PHP and R, leveraging REDCap's Data Entry Trigger functionality.
  • Automated randomizations were triggered upon saving a specific REDCap form.
  • Study personnel received automated notifications of randomization assignments.

Main Results:

  • The implemented pipeline successfully reduced covariate imbalance in the all2GETHER study.
  • Observed differences between study arms were minimal (Cohen's d = 0.003 for continuous variables, risk differences <2.4% for categorical/binary variables).
  • The integration required minimal additional effort from study personnel.

Conclusions:

  • The developed software pipeline effectively reduces covariate imbalance in RCTs conducted using REDCap.
  • The pipeline is reproducible and offers a practical solution for other research studies.
  • This integration enhances the efficiency and comparability of treatment arms in clinical trials.