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Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availabilityandimplementation<\/jats:title><jats:p>https:\/\/github.com\/rivas-lab\/multisnpnet-Cox<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab095","type":"journal-article","created":{"date-parts":[[2021,2,6]],"date-time":"2021-02-06T05:21:00Z","timestamp":1612588860000},"page":"4437-4443","source":"Crossref","is-referenced-by-count":9,"title":["Survival analysis on rare events using group-regularized multi-response Cox regression"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5152-7086","authenticated-orcid":false,"given":"Ruilin","family":"Li","sequence":"first","affiliation":[{"name":"Institute for Computational and Mathematical Engineering, Stanford University , Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-157X","authenticated-orcid":false,"given":"Yosuke","family":"Tanigawa","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, USA"}]},{"given":"Johanne M","family":"Justesen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, USA"}]},{"given":"Jonathan","family":"Taylor","sequence":"additional","affiliation":[{"name":"Department of Statistics, Stanford University , Stanford, CA 94305, USA"}]},{"given":"Trevor","family":"Hastie","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, USA"},{"name":"Department of Statistics, Stanford University , Stanford, CA 94305, USA"}]},{"given":"Robert","family":"Tibshirani","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, USA"},{"name":"Department of Statistics, Stanford University , Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1457-9925","authenticated-orcid":false,"given":"Manuel A","family":"Rivas","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University , Stanford, CA 94305, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"2023061402422107900_btab095-B1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.ajhg.2019.07.001","article-title":"Phenome-wide burden of copy-number variation in the UK Biobank","volume":"105","author":"Aguirre","year":"2019","journal-title":"Am. 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