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

Handle URI:
http://hdl.handle.net/10754/599589
Title:
Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error
Authors:
Sinha, Samiran; Mallick, Bani K.; Kipnis, Victor; Carroll, Raymond J.
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.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
10-Aug-2009
DOI:
10.1111/j.1541-0420.2009.01309.x
PubMed ID:
19673858
PubMed Central ID:
PMC2888615
Type:
Article
ISSN:
0006-341X
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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorSinha, Samiranen
dc.contributor.authorMallick, Bani K.en
dc.contributor.authorKipnis, Victoren
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2016-02-28T05:53:53Zen
dc.date.available2016-02-28T05:53:53Zen
dc.date.issued2009-08-10en
dc.identifier.citationSinha 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.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid19673858en
dc.identifier.doi10.1111/j.1541-0420.2009.01309.xen
dc.identifier.urihttp://hdl.handle.net/10754/599589en
dc.description.abstractWe 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.en
dc.description.sponsorshipThe 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).en
dc.publisherWiley-Blackwellen
dc.subjectB-splinesen
dc.subjectDirichlet process prioren
dc.subjectGibbs samplingen
dc.subjectMeasurement erroren
dc.subjectMetropolis-Hastings algorithmen
dc.subjectPartly linear modelen
dc.subject.meshArtifactsen
dc.subject.meshBayes Theoremen
dc.subject.meshNutritional Statusen
dc.titleSemiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Erroren
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.identifier.pmcidPMC2888615en
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texas 77843, USA. sinha@stat.tamu.eduen
kaust.grant.numberKUS-CI-016-04en

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