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CFO: Calibration-Free Odds Bayesian Designs for Dose Finding in Clinical Trials
Jialu Fang1, Ninghao Zhang1, Wenliang Wang1
1School of Computing and Data Science, The University of Hong Kong, Hong Kong, China.
We developed user-friendly R and Shiny software for implementing advanced calibration-free odds (CFO) type designs in clinical trials. These tools enhance dose-finding accuracy and efficiency by integrating randomization and various CFO design variants.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmacometrics
Background:
- Calibration-free odds (CFO)-type designs are the current standard for robust and model-free dose-finding in clinical trials.
- A significant barrier to the widespread adoption of CFO-type designs is the lack of accessible implementation tools.
Purpose of the Study:
- To develop user-friendly R package and Shiny web-based software for the practical implementation of CFO-type designs.
- To incorporate randomization into the CFO framework, creating the randomized CFO (rCFO) design.
Main Methods:
- Developed the R package "CFO" and an interactive R Shiny web application named "CFO suite".
- Integrated an exploration-exploitation mechanism via randomization to introduce the rCFO design.
- Incorporated various CFO design variants including 2dCFO, aCFO, TITE-CFO, fCFO, TITE-aCFO, and fractional-aCFO.
Main Results:
- The CFO package and CFO suite offer a comprehensive suite of tools for various clinical trial settings.
- Functions are provided for dose determination, maximum tolerated dose selection, and performance evaluation through simulations.
- Outputs include both textual and graphical representations of simulation and trial results.
Conclusions:
- The CFO package and CFO suite provide flexible and comprehensive tools for implementing CFO-type designs in Phase I clinical trials.
- This work integrates existing CFO-type designs, enabling novel trial designs with improved performance.
- The user-friendly software promotes the adoption of advanced statistical methods and strengthens biostatistician-clinician collaboration for enhanced trial efficiency and accuracy.
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