Table 1.

Recommended methods and analyses for the development and validation of prediction models including supportive references

Methodology
Handling of missing data	It is generally advised to use multiple imputation for handling of missing data. Complete case analysis, single or mean imputation are inefficient methods to estimate coefficients.	47–49
Selection and retaining of predictors in multivariable models	Predictor selection and retaining is preferably based on clinical knowledge and previous literature, instead of significance levels in univariable or stepwise analysis.	22 26 27
Internal validation	It is advised to internally validate the model to assess optimism in performance and reduce overfitting. An efficient method is bootstrapping; split-sample validation should be avoided.	25 26
Calibration	It is advised to assess the calibration of a model at external validation. The preferred method is a calibration plot, with intercept and slope, and not statistical tests (eg, Hosmer-Lemeshow), as a plot retains the most information on possible miscalibration.	22 26 27 50
External validation	External validation of models is needed for rigorous assessment of performance. The preferred external validation population is fully independent.	28 51