Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

Handle URI:
http://hdl.handle.net/10754/597654
Title:
Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements
Authors:
Ryu, Duchwan; Li, Erning; Mallick, Bani K.
Abstract:
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
Citation:
Ryu D, Li E, Mallick BK (2010) Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements. Biometrics 67: 454–466. Available: http://dx.doi.org/10.1111/j.1541-0420.2010.01489.x.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
28-Sep-2010
DOI:
10.1111/j.1541-0420.2010.01489.x
PubMed ID:
20880012
Type:
Article
ISSN:
0006-341X
Sponsors:
The research of D. Ryu and B. Mallick was partially supported by Award Number KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), and was partially supported by the DOE NNSA under the Predictive Science Academic Alliances Program by grant DE-FC52-08NA28616. B. Mallick's research was also partially supported by National Science Foundation grant NSF DMS 0914951 and National Cancer Institute grant CA 57030. The authors are thankful to M. Pepe and K. Seidel for the child growth data, and S. Berry for the Fortran source code of the Bayesian smoothing splines.
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Full metadata record

DC FieldValue Language
dc.contributor.authorRyu, Duchwanen
dc.contributor.authorLi, Erningen
dc.contributor.authorMallick, Bani K.en
dc.date.accessioned2016-02-25T12:43:48Zen
dc.date.available2016-02-25T12:43:48Zen
dc.date.issued2010-09-28en
dc.identifier.citationRyu D, Li E, Mallick BK (2010) Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements. Biometrics 67: 454–466. Available: http://dx.doi.org/10.1111/j.1541-0420.2010.01489.x.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid20880012en
dc.identifier.doi10.1111/j.1541-0420.2010.01489.xen
dc.identifier.urihttp://hdl.handle.net/10754/597654en
dc.description.abstractWe consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.en
dc.description.sponsorshipThe research of D. Ryu and B. Mallick was partially supported by Award Number KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), and was partially supported by the DOE NNSA under the Predictive Science Academic Alliances Program by grant DE-FC52-08NA28616. B. Mallick's research was also partially supported by National Science Foundation grant NSF DMS 0914951 and National Cancer Institute grant CA 57030. The authors are thankful to M. Pepe and K. Seidel for the child growth data, and S. Berry for the Fortran source code of the Bayesian smoothing splines.en
dc.publisherWiley-Blackwellen
dc.subjectBayesian nonparametric regressionen
dc.subjectData augmentationen
dc.subjectGeneralized additive modelen
dc.subjectLongitudinal dataen
dc.subjectMeasurement erroren
dc.titleBayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurementsen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.contributor.institutionMedical College of Georgia, Augusta, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en

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