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A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.

Hornung R, Bernau C, Truntzer C, Wilson R, Stadler T, Boulesteix AL - BMC Med Res Methodol (2015)

Bottom Line: Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration.Furthermore we obtain preliminary results for the following steps: optimization of tuning parameters, variable filtering by variance and imputation of missing values.In contrast, when performing PCA before CV, medium to strong underestimates of the prediction error were observed in multiple settings.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, D-81377, Germany. hornung@ibe.med.uni-muenchen.de.

ABSTRACT

Background: In applications of supervised statistical learning in the biomedical field it is necessary to assess the prediction error of the respective prediction rules. Often, data preparation steps are performed on the dataset-in its entirety-before training/test set based prediction error estimation by cross-validation (CV)-an approach referred to as "incomplete CV". Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration. Several empirical studies have investigated the extent of bias induced by performing preliminary supervised variable selection before CV. To our knowledge, however, the potential bias induced by other data preparation steps has not yet been examined in the literature. In this paper we investigate this bias for two common data preparation steps: normalization and principal component analysis for dimension reduction of the covariate space (PCA). Furthermore we obtain preliminary results for the following steps: optimization of tuning parameters, variable filtering by variance and imputation of missing values.

Methods: We devise the easily interpretable and general measure CVIIM ("CV Incompleteness Impact Measure") to quantify the extent of bias induced by incomplete CV with respect to a data preparation step of interest. This measure can be used to determine whether a specific data preparation step should, as a general rule, be performed in each CV iteration or whether an incomplete CV procedure would be acceptable in practice. We apply CVIIM to large collections of microarray datasets to answer this question for normalization and PCA.

Results: Performing normalization on the entire dataset before CV did not result in a noteworthy optimistic bias in any of the investigated cases. In contrast, when performing PCA before CV, medium to strong underestimates of the prediction error were observed in multiple settings.

Conclusions: While the investigated forms of normalization can be safely performed before CV, PCA has to be performed anew in each CV split to protect against optimistic bias.

No MeSH data available.


Errors in PCA study. efull,K(s)- and eincompl,K(s)-values for all datasets and settings from the PCA study
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Fig5: Errors in PCA study. efull,K(s)- and eincompl,K(s)-values for all datasets and settings from the PCA study

Mentions: An obvious, but less insightful, way of visualizing the impact of CV incompleteness, is to simply plot efull,K(s) and eincompl,K(s) for the individual datasets. Figure 5 shows such a plot for the PCA study. Without closer inspection we observe that in some cases eincompl,K(s) is considerably smaller than efull,K(s), indicating the strong bias already suggested by the CVIIMs,n,K-values.Fig. 5


A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.

Hornung R, Bernau C, Truntzer C, Wilson R, Stadler T, Boulesteix AL - BMC Med Res Methodol (2015)

Errors in PCA study. efull,K(s)- and eincompl,K(s)-values for all datasets and settings from the PCA study
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4634762&req=5

Fig5: Errors in PCA study. efull,K(s)- and eincompl,K(s)-values for all datasets and settings from the PCA study
Mentions: An obvious, but less insightful, way of visualizing the impact of CV incompleteness, is to simply plot efull,K(s) and eincompl,K(s) for the individual datasets. Figure 5 shows such a plot for the PCA study. Without closer inspection we observe that in some cases eincompl,K(s) is considerably smaller than efull,K(s), indicating the strong bias already suggested by the CVIIMs,n,K-values.Fig. 5

Bottom Line: Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration.Furthermore we obtain preliminary results for the following steps: optimization of tuning parameters, variable filtering by variance and imputation of missing values.In contrast, when performing PCA before CV, medium to strong underestimates of the prediction error were observed in multiple settings.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Marchioninistr. 15, Munich, D-81377, Germany. hornung@ibe.med.uni-muenchen.de.

ABSTRACT

Background: In applications of supervised statistical learning in the biomedical field it is necessary to assess the prediction error of the respective prediction rules. Often, data preparation steps are performed on the dataset-in its entirety-before training/test set based prediction error estimation by cross-validation (CV)-an approach referred to as "incomplete CV". Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration. Several empirical studies have investigated the extent of bias induced by performing preliminary supervised variable selection before CV. To our knowledge, however, the potential bias induced by other data preparation steps has not yet been examined in the literature. In this paper we investigate this bias for two common data preparation steps: normalization and principal component analysis for dimension reduction of the covariate space (PCA). Furthermore we obtain preliminary results for the following steps: optimization of tuning parameters, variable filtering by variance and imputation of missing values.

Methods: We devise the easily interpretable and general measure CVIIM ("CV Incompleteness Impact Measure") to quantify the extent of bias induced by incomplete CV with respect to a data preparation step of interest. This measure can be used to determine whether a specific data preparation step should, as a general rule, be performed in each CV iteration or whether an incomplete CV procedure would be acceptable in practice. We apply CVIIM to large collections of microarray datasets to answer this question for normalization and PCA.

Results: Performing normalization on the entire dataset before CV did not result in a noteworthy optimistic bias in any of the investigated cases. In contrast, when performing PCA before CV, medium to strong underestimates of the prediction error were observed in multiple settings.

Conclusions: While the investigated forms of normalization can be safely performed before CV, PCA has to be performed anew in each CV split to protect against optimistic bias.

No MeSH data available.