4 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Lasai Barreñada, Ewout W Steyerberg, Dirk Timmerman +3
Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view re…
Clustered Flexible Calibration Plots For Binary Outcomes Using Random Effects Modeling
Lasai Barreñada, Bavo D. C. Campo, Laure Wynants +1
Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment o…
missForestPredict -- Missing data imputation for prediction settings
Elena Albu, Shan Gao, Laure Wynants +1
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestP…
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
Richard D Riley, Gary S Collins, Rebecca Whittle +11
When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instabil…