Training replicable predictors in multiple studies

Author:

Patil Prasad,Parmigiani GiovanniORCID

Abstract

This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations.

Funder

HHS | NIH | National Cancer Institute

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

Reference48 articles.

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