Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies.

ABSTRACT: 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 comp

SUBMITTER: Krautenbacher N 

PROVIDER: S-EPMC5632994 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature