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    Multiple imputation for estimating the risk of developing dementia and its impact on survival

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    Authors
    Yu, Binbing
    Saczynski, Jane S.
    Launer, Lenore J.
    UMass Chan Affiliations
    Meyers Primary Care Institute
    Department of Quantitative Health Sciences
    Department of Medicine, Division of Geriatric Medicine
    Document Type
    Journal Article
    Publication Date
    2010-10-27
    Keywords
    Biostatistics
    Epidemiology
    Geriatrics
    Health Services Research
    Nervous System Diseases
    
    Metadata
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    Link to Full Text
    http://dx.doi.org/10.1002/bimj.200900266
    Abstract
    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.
    Source
    Biom J. 2010 Oct;52(5):616-27. Link to article on publisher's site
    DOI
    10.1002/bimj.200900266
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/47849
    PubMed ID
    20976693
    Related Resources
    Link to Article in PubMed
    ae974a485f413a2113503eed53cd6c53
    10.1002/bimj.200900266
    Scopus Count
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    Population and Quantitative Health Sciences Publications

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