Bayesian Decision Curve Analysis With Bayesdca
Giuliano Netto Flores Cruz, Keegan Korthauer · 2024
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Description
Giuliano Netto Flores Cruz and Keegan Korthauer present a Bayesian take on decision curve analysis, a method for judging whether a medical test or model is worth using, in a 2024 paper. The method weighs the good of true positive calls against the harm of false ones, giving a net benefit score. Their version adds probability statements and prior evidence to that judgment.
The Bayesian framing answers four questions: which strategies help at all, which is best, which of two rivals wins, and how much benefit may be lost to current doubt. Results often match the classic point estimates, but now come with direct odds for each claim. The authors test the approach in simulations, walk through a full case study, and share it as an R software package called bayesDCA.
Catalog record
| CREATOR | Giuliano Netto Flores Cruz, Keegan Korthauer |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 71 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |