Correcting classifiers for sample selection bias in two-phase case-control studies
type: Item
instance of: scholarly article
title: Correcting classifiers for sample selection bias in two-phase case-control studies
zbMATH Open document ID: 1398.92012
DOI: 10.1155/2017/7847531
author: Norbert Krautenbacher
author: Fabian J. Theis
author: Christiane Fuchs
published in: Computational \& Mathematical Methods in Medicine
publication date: 2018-09-26T00:00:00Z
review text: Summary: Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia.
Mathematics Subject Classification ID: 92B15
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62D05
Mathematics Subject Classification ID: 92D30
zbMATH DE Number: 6943940
zbMATH Keywords: sample selection bias
zbMATH Keywords: epidemiology
zbMATH Keywords: two-phase case-control studies
zbMATH Keywords: classifiers
Wikidata QID: Q47193912
describes a project that uses: ROCR
describes a project that uses: smotefamily
describes a project that uses: R
describes a project that uses: e1071
describes a project that uses: Survey
describes a project that uses: ranger
describes a project that uses: OpenML
describes a project that uses: SMOTE
describes a project that uses: pROC
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/7847531
OpenAlex ID: W2757523618
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description: scientific article; zbMATH DE number 6943940