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In assessing prediction accuracy of multivariable prediction models, optimismcorrections are essential for preventing biased results. However, in mostpublished papers of clinical prediction models, the point estimates of theprediction accuracy measures are corrected by adequate bootstrap-basedcorrection methods, but their confidence intervals are not corrected, e.g., theDeLong's confidence interval is usually used for assessing the C-statistic.These naive methods do not adjust for the optimism bias and do not account forstatistical variability in the estimation of parameters in the predictionmodels. Therefore, their coverage probabilities of the true value of theprediction accuracy measure can be seriously below the nominal level (e.g.,95%). In this article, we provide two generic bootstrap methods, namely (1)location-shifted bootstrap confidence intervals and (2) two-stage bootstrapconfidence intervals, that can be generally applied to the bootstrap-basedoptimism correction methods, i.e., the Harrell's bias correction, 0.632, and0.632+ methods. In addition, they can be widely applied to various methods forprediction model development involving modern shrinkage methods such as theridge and lasso regressions. Through numerical evaluations by simulations, theproposed confidence intervals showed favourable coverage performances. Besides,the current standard practices based on the optimism-uncorrected methods showedserious undercoverage properties. To avoid erroneous results, theoptimism-uncorrected confidence intervals should not be used in practice, andthe adjusted methods are recommended instead. We also developed the R packagepredboot for implementing these methods (https://github.com/nomahi/predboot).The effectiveness of the proposed methods are illustrated via applications tothe GUSTO-I clinical trial.

Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap‐based optimism correction methods