RT Journal Article
SR Electronic
T1 High-dimensional regression over disease subgroups
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 092825
DO 10.1101/092825
A1 Frank Dondelinger
A1 Sach Mukherjee
A1 The Alzheimer’s Disease Neuroimaging Initiative
YR 2016
UL http://biorxiv.org/content/early/2016/12/09/092825.abstract
AB We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where disease subtypes, for example, may differ with respect to underlying regression models, but sample sizes at the subgroup-level may be limited. We focus on the case in which subgroup-specific models may be expected to be similar but not necessarily identical. Our approach is to treat subgroups as related problem instances and jointly estimate subgroup-specific regression coefficients. This is done in a penalized framework, combining an ℓ1 term with an additional term that penalizes differences between subgroup-specific coefficients. This gives solutions that are globally sparse but that allow information-sharing between the subgroups. We present algorithms for estimation and empirical results on simulated data and using Alzheimer’s disease, amyotrophic lateral sclerosis and cancer datasets. These examples demonstrate the gains our approach can offer in terms of prediction and the ability to estimate subgroup-specific sparsity patterns.