RRApp, a robust randomization app, for clinical and translational research.

Area of Science:

• Clinical Research
• Biostatistics
• Medical Informatics

Background:

• Junior clinical researchers often lack resources and capacity for designing randomized-clinical trials.
• Insufficient research capacity hinders the generation of rigorous randomization schemes, potentially introducing selection bias.
• Limited availability of user-friendly online randomization tools poses a challenge for early-stage clinical trialists.

Purpose of the Study:

• To develop a free, robust, and user-friendly randomization app (RRApp) for junior clinical researchers.
• To provide accessible tools for generating reproducible randomization schemes, thereby minimizing selection bias.
• To educate early-stage clinical trialists on the importance and implementation of randomization techniques.

Main Methods:

• Developed RRApp, a free randomization application incorporating six major randomization techniques: simple, stratified, block, permuted block, stratified block, and stratified permuted block randomization.
• Designed RRApp with robust server scripts and a user-friendly interface using the R "shiny" package.
• Ensured generated randomization schemes are compatible with the Research Electronic Data Capture (REDCap) system.

Main Results:

• Completed the design phase of RRApp, including server scripts and a user interface.
• RRApp successfully incorporates six distinct randomization techniques.
• The app's output is directly compatible with REDCap, streamlining data management.

Conclusions:

• RRApp provides a valuable, free resource for junior clinical researchers to design and implement robust randomization schemes.
• The app addresses the need for user-friendly tools, enhancing research capacity and minimizing bias in clinical trials.
• Feedback from biostatisticians and clinical faculty indicates RRApp's quality and functionality, supporting its educational and practical utility.