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Methodologic Issues Specific to Prediction Model Development and Evaluation
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AI
DOI：10.1016/j.chest.2023.06.038.png)
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Abstract
En 中文
Developing and evaluating statistical prediction models is challenging, and many pitfalls can arise. This article identifies what the authors believe are some common methodologic concerns that may be encountered. We describe each problem and make suggestions regarding how to address them. The hope is that this article will result in higher-quality publications of statistical
Keywords:
KEY WORDS
calibration
continuous predictors
Cox regression
cross-validation
Hosmer-Lemeshow test
index of prediction accuracy
model development
rare outcome
ROC curve
SHAP value
time to event end point
unbalanced data
variable selection