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However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying\u2010coefficient regression. However, this is challenging when there is a pre\u2010specified group structure among variables. We propose a novel variable selection method for a varying\u2010coefficient regression with such structured variables and provide a publicly available R package <jats:styled-content>svreg<\/jats:styled-content> for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease\u00a0severity.<\/jats:p>","DOI":"10.1002\/bimj.202000312","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T23:05:11Z","timestamp":1618873511000},"page":"1254-1271","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["svReg: Structural varying\u2010coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9577-7872","authenticated-orcid":false,"given":"Rakheon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Statistics Texas A&amp;M University TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics Macquarie University Sydney Australia"},{"name":"School of Mathematics and Statistics The University of Sydney Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanya P.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Gillings School of Public Health UNC Chapel Hill Chapel Hill NC USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3233\/JHD-130076"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_2_9_4_1","first-page":"1","article-title":"Tree\u2010structured modelling of varying coefficients","volume":"29","author":"Berger M.","year":"2017","journal-title":"Statistics and Computing"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1096"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22601"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2015.01.003"},{"key":"e_1_2_9_8_1","unstructured":"Du W. &Tibshirani R.(2018).A pliable lasso for the Cox model.arXiv preprint arXiv:1807.06770."},{"key":"e_1_2_9_9_1","unstructured":"Friedman J. 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