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Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing

Gabriel Loewinger, Prasad Patil, Kenneth Kishida, Giovanni Parmigiani
doi: https://doi.org/10.1101/856385
Gabriel Loewinger
¶Harvard University
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  • For correspondence: gloewinger@g.harvard.edu
Prasad Patil
‖Boston University
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Kenneth Kishida
**Wake Forest University
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Giovanni Parmigiani
††Dana Farber Cancer Institute
¶Harvard University
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Abstract

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 26, 2019.
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Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing
Gabriel Loewinger, Prasad Patil, Kenneth Kishida, Giovanni Parmigiani
bioRxiv 856385; doi: https://doi.org/10.1101/856385
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Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing
Gabriel Loewinger, Prasad Patil, Kenneth Kishida, Giovanni Parmigiani
bioRxiv 856385; doi: https://doi.org/10.1101/856385

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