First, full disclosure that I am now employed by BioData Solutions so conflict of interest, but I was a client before I became a consultant.
The AI behind Red Thread does follow the Shankar approach, and is not the probability based generative AI. Instead it fully automates the process of outlier removal and statistics calculations so that you can have your cut point factor in less than an hour rather than waiting weeks for statistician availability and manual effort. It also has a full audit trail to enable QC and can be repeated by a second individual to demonstrate the same answer. So the advantage of AI in this case is 1) speed and 2) traceability lacking in many statistical software in use.
That said, I concur with John that the technology isn't the challenge here, it's the difference from the validation population and the in-study population. Evaluating the baseline samples from the study to establish a study-specific cut point factor is likely the best approach. If a very small study, 13% isn't really that much higher than the expected 2-11% range even with a perfect cut point factor, so if testing more samples in confirmatory than expected isn't a great burden, I wouldn't necessarily worry about a higher false positive rate.
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Joleen White Ph.D.
AAPS 2024 Global Health Community Chair
Bioanalytical 101 Course Development
Senior Advisor
BioData Solutions LLC
[email protected]Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer.
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Original Message:
Sent: 07-14-2025 23:20
From: Anonymous Member
Subject: Use of Red Thread statistical analysis
This message was posted by a user wishing to remain anonymous
Hello,
I am wondering how the community is utilizing red thread to ensure the Screening cut point is 5%FPR? This approach is being considered as the FPR is grater than 13%.
Any challenges associated with utilizing the approach?
Thank you!