{"data":{"id":"10.6084/m9.figshare.c.2058572","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2058572","prefix":"10.6084","suffix":"m9.figshare.c.2058572","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Yan, Ying","givenName":"Ying","familyName":"Yan","affiliation":[],"nameIdentifiers":[]},{"name":"Y. Yi, Grace","givenName":"Grace","familyName":"Y. Yi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"A Class of Functional Methods for Error-Contaminated Survival Data under Additive Hazards Models with Replicate Measurements"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Science Policy"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences"},{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biotechnology"},{"subject":"Genetics"},{"subject":"Cell Biology"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2019-08-19","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Covariate measurement error has attracted extensive interest in survival analysis. Since Prentice (1982), a large number of inference methods have been developed to handle error-prone data that are modulated with proportional hazards models. In contrast to proportional hazards models, additive hazards models offer a flexible tool to delineate survival processes. However, there is little research on measurement error effects under additive hazards models. In this paper, we systematically investigate this important problem. New insights of measurement error effects are revealed, as opposed to well-documented results for proportional hazards models. In particular, we explore asymptotic bias of ignoring measurement error in the analysis. To correct for the induced bias, we develop a class of functional correction methods for error effects. The validity of the proposed methods is carefully examined, and we investigate issues of model checking and model misspecification. Theoretical results are rigorously established, and are complemented with numerical assessments.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/collections/A_Class_of_Functional_Methods_for_Error_Contaminated_Survival_Data_under_Additive_Hazards_Models_with_Replicate_Measurements/2058572","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-12-05T17:37:14.000Z","registered":"2015-12-05T17:37:15.000Z","published":"2015","updated":"2024-03-21T08:41:55.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.c.2058572","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}