Sample size is so very neglected in most prediction model research
Here is a paper from our team that guides researchers toward sample sizes for precise performance estimates in validation studies
And pmvalsampsize is your one line of code to implement it! By @joie_ensor
How should researchers undertake an external validation study?
This article guides readers through the steps involved @Richard_D_Riley https://t.co/Ub2Wlj0lw8
Very excited to share a new work on AUPRC, AUROC, and class imbalance: https://t.co/QBm6Khb3Ei
The short of it? Despite widespread belief, AUPRC is not superior to AUROC under general class imbalance and it can exacerbate biases towards subpopulations with higher prevalence.
Reminded today about how important this paper is by @laure_wynants@BenVanCalster@ESteyerberg@MaartenvSmeden et al
"Three myths about risk thresholds for prediction models"
Highly recommended reading
https://t.co/T0DRaSMbnQ
NEW PAPER in @bmj_latest (with @Richard_D_Riley) - 1st in a 3 part series on the ‘Evaluation of clinical prediction models’. Part 1 is ‘from development to external validation’.
—> https://t.co/HJM4oukAyY
⭐️NEW paper
“Clinical prediction models and the multiverse of madness”
For any model created, a multiverse of other potential models exists & an individual’s prediction may vary greatly across this multiverse
Avengers endgame: examine this instability🙏
https://t.co/djRZwLASN0
In a new Perspective, Walia, Tuia & @VPrasadMDMPH discuss the adoption of composite end points in oncology, their pro and cons, and advocate for separate reporting of component events to enable a better understanding of cancer biology and trial results: https://t.co/5LckcxOS6p