Validation of prediction models based on lasso regression with multiply imputed data

Jammbe Z Musoro, Aeilko H Zwinderman, Milo A Puhan, Gerben ter Riet, Ronald B Geskus

In prognostic studies, the lasso technique is attractive since it improves the quality of predictions by shrinking regression coefficients, compared to predictions based on a model fitted via unpenalized maximum likelihood. Since some coefficients are set to zero, parsimony is achieved as well. It is unclear whether the performance of a model fitted using the lasso still shows some optimism. Bootstrap methods have been advocated to quantify optimism and generalize model performance to new subjects. It is unclear how resampling should be performed in the presence of multiply imputed data.