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dc.contributor.authorYu, Binbing
dc.contributor.authorSaczynski, Jane S.
dc.contributor.authorLauner, Lenore J.
dc.date2022-08-11T08:10:44.000
dc.date.accessioned2022-08-23T17:18:25Z
dc.date.available2022-08-23T17:18:25Z
dc.date.issued2010-10-27
dc.date.submitted2011-02-01
dc.identifier.citationBiom J. 2010 Oct;52(5):616-27. <a href="http://dx.doi.org/10.1002/bimj.200900266">Link to article on publisher's site</a>
dc.identifier.issn0323-3847 (Linking)
dc.identifier.doi10.1002/bimj.200900266
dc.identifier.pmid20976693
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47849
dc.description.abstractDementia, Alzheimer's disease in particular, is one of the major causes of disability and decreased quality of life among the elderly and a leading obstacle to successful aging. Given the profound impact on public health, much research has focused on the age-specific risk of developing dementia and the impact on survival. Early work has discussed various methods of estimating age-specific incidence of dementia, among which the illness-death model is popular for modeling disease progression. In this article we use multiple imputation to fit multi-state models for survival data with interval censoring and left truncation. This approach allows semi-Markov models in which survival after dementia depends on onset age. Such models can be used to estimate the cumulative risk of developing dementia in the presence of the competing risk of dementia-free death. Simulations are carried out to examine the performance of the proposed method. Data from the Honolulu Asia Aging Study are analyzed to estimate the age-specific and cumulative risks of dementia and to examine the effect of major risk factors on dementia onset and death.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=20976693&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1002/bimj.200900266
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectGeriatrics
dc.subjectHealth Services Research
dc.subjectNervous System Diseases
dc.titleMultiple imputation for estimating the risk of developing dementia and its impact on survival
dc.typeJournal Article
dc.source.journaltitleBiometrical journal. Biometrische Zeitschrift
dc.source.volume52
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/961
dc.identifier.contextkey1755884
html.description.abstract<p>Dementia, Alzheimer's disease in particular, is one of the major causes of disability and decreased quality of life among the elderly and a leading obstacle to successful aging. Given the profound impact on public health, much research has focused on the age-specific risk of developing dementia and the impact on survival. Early work has discussed various methods of estimating age-specific incidence of dementia, among which the illness-death model is popular for modeling disease progression. In this article we use multiple imputation to fit multi-state models for survival data with interval censoring and left truncation. This approach allows semi-Markov models in which survival after dementia depends on onset age. Such models can be used to estimate the cumulative risk of developing dementia in the presence of the competing risk of dementia-free death. Simulations are carried out to examine the performance of the proposed method. Data from the Honolulu Asia Aging Study are analyzed to estimate the age-specific and cumulative risks of dementia and to examine the effect of major risk factors on dementia onset and death.</p>
dc.identifier.submissionpathqhs_pp/961
dc.contributor.departmentMeyers Primary Care Institute
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.contributor.departmentDepartment of Medicine, Division of Geriatric Medicine
dc.source.pages616-27


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