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Flexible co‐data learning for high‐dimensional prediction
Author(s) -
Nee Mirrelijn M.,
Wessels Lodewyk F.A.,
Wiel Mark A.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9162
Subject(s) - covariate , overfitting , computer science , hyperparameter , feature selection , weighting , bayes' theorem , bayesian probability , data mining , machine learning , artificial intelligence , medicine , artificial neural network , radiology
Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high‐dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co‐data, provide information on the covariates, such as genomic location or P ‐values from external studies. We use multiple and various co‐data to define possibly overlapping or hierarchically structured groups of covariates. These are then used to estimate adaptive multi‐group ridge penalties for generalized linear and Cox models. Available group adaptive methods primarily target for settings with few groups, and therefore likely overfit for non‐informative, correlated or many groups, and do not account for known structure on group level. To handle these issues, our method combines empirical Bayes estimation of the hyperparameters with an extra level of flexible shrinkage. This renders a uniquely flexible framework as any type of shrinkage can be used on the group level. We describe various types of co‐data and propose suitable forms of hypershrinkage. The method is very versatile, as it allows for integration and weighting of multiple co‐data sets, inclusion of unpenalized covariates and posterior variable selection. For three cancer genomics applications we demonstrate improvements compared to other models in terms of performance, variable selection stability and validation.

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