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• W2096561439 abstract "Abstract Motivation: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings. Cross-validation within exemplary datasets may not adequately reflect performance in the broader application context. Methods: We develop and implement a systematic approach to ‘cross-study validation’, to replace or supplement conventional cross-validation when evaluating high-dimensional prediction models in independent datasets. We illustrate it via simulations and in a collection of eight estrogen-receptor positive breast cancer microarray gene-expression datasets, where the objective is predicting distant metastasis-free survival (DMFS). We computed the C-index for all pairwise combinations of training and validation datasets. We evaluate several alternatives for summarizing the pairwise validation statistics, and compare these to conventional cross-validation. Results: Our data-driven simulations and our application to survival prediction with eight breast cancer microarray datasets, suggest that standard cross-validation produces inflated discrimination accuracy for all algorithms considered, when compared to cross-study validation. Furthermore, the ranking of learning algorithms differs, suggesting that algorithms performing best in cross-validation may be suboptimal when evaluated through independent validation. Availability: The survHD: Survival in High Dimensions package (http://www.bitbucket.org/lwaldron/survhd) will be made available through Bioconductor. Contact: levi.waldron@hunter.cuny.edu Supplementary information: Supplementary data are available at Bioinformatics online." @default.
• W2096561439 created "2016-06-24" @default.
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• W2096561439 date "2014-06-11" @default.
• W2096561439 modified "2023-10-16" @default.
• W2096561439 title "Cross-study validation for the assessment of prediction algorithms" @default.
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• W2096561439 doi "https://doi.org/10.1093/bioinformatics/btu279" @default.
• W2096561439 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4058929" @default.
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