RT Journal Article
SR Electronic
T1 Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 856385
DO 10.1101/856385
A1 Gabriel Loewinger
A1 Prasad Patil
A1 Kenneth Kishida
A1 Giovanni Parmigiani
YR 2019
UL http://biorxiv.org/content/early/2019/11/26/856385.abstract
AB Prediction settings with multiple studies have become increasingly common. Ensembling models trained on individual studies has been shown to improve replicability in new studies. Motivated by a groundbreaking new technology in human neuroscience, we introduce two generalizations of multi-study ensemble predictions. First, while existing methods weight ensemble elements by cross-study prediction performance, we extend weighting schemes to also incorporate covariate similarity between training data and target validation studies. Second, we introduce a hierarchical resampling scheme to generate pseudo-study replicates (“study straps”) and ensemble classifiers trained on these rather than the original studies themselves. We demonstrate analytically that existing methods are special cases. Through a tuning parameter, our approach forms a continuum between merging all training data and training with existing multi-study ensembles. Leveraging this continuum helps accommodate different levels of between-study heterogeneity.Our methods are motivated by the application of Voltammetry in humans. This technique records electrical brain measurements and converts signals into neurotransmitter concentration estimates using a prediction model. Using this model in practice presents a cross-study challenge, for which we show marked improvements after application of our methods. We verify our methods in simulations and provide the studyStrap R package.