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    Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

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    Type
    Article
    Authors
    Sinha, Samiran
    Mallick, Bani K.
    Kipnis, Victor
    Carroll, Raymond J.
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2009-08-10
    Online Publication Date
    2009-08-10
    Print Publication Date
    2010-06
    Permanent link to this record
    http://hdl.handle.net/10754/599589
    
    Metadata
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    Abstract
    We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B-splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH-AARP Diet and Health Study and examine its performance in a simulation study.
    Citation
    Sinha S, Mallick BK, Kipnis V, Carroll RJ (2009) Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error. Biometrics 66: 444–454. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01309.x.
    Sponsors
    The research of BKM and RJC was supported by grants from the National Cancer Institute (CA57030, CA 104620) and in part by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Wiley
    Journal
    Biometrics
    DOI
    10.1111/j.1541-0420.2009.01309.x
    PubMed ID
    19673858
    PubMed Central ID
    PMC2888615
    ae974a485f413a2113503eed53cd6c53
    10.1111/j.1541-0420.2009.01309.x
    Scopus Count
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