{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:28:45Z","timestamp":1762918125820},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We consider an extension of Aalen\u2019s additive regression model that allows covariates to have effects that vary on two different time scales. The two time scales considered are equal up to a constant for each individual and vary across individuals, such as follow-up time and age in medical studies or calendar time and age in longitudinal studies. The model was introduced in Scheike (2001), where it was solved using smoothing techniques. We present a new backfitting algorithm for estimating the structured model without having to use smoothing. Estimators of the cumulative regression functions on the two time scales are suggested by solving local estimating equations jointly on the two time scales. We provide large-sample properties and simultaneous confidence bands. The model is applied to data on myocardial infarction, providing a separation of the two effects stemming from time since diagnosis and age.<\/jats:p>","DOI":"10.1093\/biomet\/asaa058","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T19:17:49Z","timestamp":1593458269000},"page":"491-506","source":"Crossref","is-referenced-by-count":3,"title":["Nonsmooth backfitting for the excess risk additive regression model with two survival time scales"],"prefix":"10.1093","volume":"108","author":[{"given":"M","family":"Hiabu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, University of Sydney, Camperdown, New South Wales 2006, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J P","family":"Nielsen","sequence":"additional","affiliation":[{"name":"Cass Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, U.K"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T H","family":"Scheike","sequence":"additional","affiliation":[{"name":"Section of Biostatistics, Department of Public Health, University of Copenhagen, \u00d8ster Farimagsgade 5B, 1014 Copenhagen K, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"2021051519491672500_B1","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1002\/sim.4780080803","article-title":"A linear regression model for the analysis of life times","volume":"8","author":"Aalen,","year":"1989","journal-title":"Statist. 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