Flexible co-data learning for high-dimensional prediction

Mirrelijn M. van Nee, Lodewyk F.A. Wessels, Mark A. van de Wiel

Research output: Contribution to journalArticleScientificpeer-review

3 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalStatistics in Medicine
DOIs
Publication statusPublished - 2021

Keywords

  • clinical prediction
  • empirical Bayes
  • omics
  • penalized generalized linear models
  • prior information

Fingerprint

Dive into the research topics of 'Flexible co-data learning for high-dimensional prediction'. Together they form a unique fingerprint.

Cite this