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

IF 3.4 Q2 HEALTH CARE SCIENCES & SERVICES

JAMIA Open Pub Date : 2025-10-01 DOI:10.1093/jamiaopen/ooaf110

Jacob M Schauer, Marc O Broxton, Luke V Rasmussen, Gregory Swann, Michael E Newcomb, Jody D Ciolino

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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.

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在REDCap中通过数据输入触发器实现协变量自适应随机化自动化的工作流程。

目的：协变量自适应随机化算法（CARAs）可以减少随机对照试验（rct）中的协变量不平衡，但缺乏与研究电子数据捕获（REDCap）的集成限制了其使用。我们开发了一个软件管道，将CARAs无缝集成到REDCap中，作为all2together研究的一部分，这是一项关于艾滋病毒预防的双臂随机对照试验。材料和方法：利用REDCap的数据输入触发器和一个单独的服务器，我们在PHP和R中实现了all2together的自动随机化软件。随机化是通过保存特定的REDCap表格触发的，并自动传达给非盲研究人员。结果：研究组具有高度可比性，连续变量和风险差异的协变量差异以Cohen’s d = 0.003为特征。结论：我们的管道证明在减少协变量不平衡方面有效，研究人员的额外努力最少。讨论：这个管道是可重复的，可以被其他通过REDCap收集数据的随机对照试验使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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