Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error
Type
ArticleKAUST Grant Number
KUS-CI-016-04Date
2009-08-10Online Publication Date
2009-08-10Print Publication Date
2010-06Permanent link to this record
http://hdl.handle.net/10754/599589
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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
WileyJournal
BiometricsPubMed ID
19673858PubMed Central ID
PMC2888615ae974a485f413a2113503eed53cd6c53
10.1111/j.1541-0420.2009.01309.x
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