Ranking bias was addressed by Sun and Bull [2005], who used bootstrapping and cross-validation to correct for the bias of point estimates for the top-ranking SNP.
The selection of a pruned sub-tree can be based on a re-sampling technique (cross-validation) to correct for over-optimism due to split point optimization [ 6].
5Bound et al. (2001) page 3738, discuss an extension of this approach that uses internal validation data to correct for differential measurement error.
We explored the utility of this validation study to correct our health effect estimates for the difference between personal and ambient measures of BS, NO2, and PM2.5.
Similar to studies using validation data to correct measurement errors, as well as to other studies using regional data sets, the generalizability of the results of this study is limited by the characteristics of the data set.
Internal and external validation is required to correct for this overfit bias, also known as optimism.
Bootstrap validation was also performed to correct the estimation.
Information from a previous validation study was used to correct the effect estimates for measurement error.
A leave-one-out cross-validation (LOOCV) was performed to correct potential over-optimization for all models in parallel.
We therefore recommend repeated 10-fold cross-validation as a method to correct decision curves created using the same data as that used to generate the model.
In the latter case, internal or external validation data are usually used to correct for experimental bias that may be related to measurement, instrument or sampling design [ 28].
