{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:05:06Z","timestamp":1740135906594,"version":"3.37.3"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["F31HD102159"],"award-info":[{"award-number":["F31HD102159"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institutes on Aging"},{"name":"Religious Orders Study","award":["P30AG010161","R01AG015819"],"award-info":[{"award-number":["P30AG010161","R01AG015819"]}]},{"name":"Rush Memory and Aging Project","award":["R01AG017917"],"award-info":[{"award-number":["R01AG017917"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer\u2019s disease and dementia.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxac052","type":"journal-article","created":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T22:54:15Z","timestamp":1673132055000},"page":"449-467","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing quantile-varying covariate effects under the accelerated failure time model"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8813-9127","authenticated-orcid":false,"given":"Harrison T","family":"Reeder","sequence":"first","affiliation":[{"name":"Massachusetts General Hospital Biostatistics, , 50 Staniford Street, Suite 560, Boston, MA 02114, USA and , 25 Shattuck Street, Boston, MA 02115, USA"},{"name":"Department of Medicine, Harvard Medical School Biostatistics, , 50 Staniford Street, Suite 560, Boston, MA 02114, USA and , 25 Shattuck Street, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyu Ha","family":"Lee","sequence":"additional","affiliation":[{"name":"Departments of Nutrition, Epidemiology, and Biostatistics, Harvard T.H. Chan School of Public Health , 677 Huntington Ave, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastien","family":"Haneuse","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. 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