A semiparametric risk score for physical activity.

Cui, E; Thompson, EC; Carroll, RJ; Ruppert, D

A semiparametric risk score for physical activity.

Cui, E Thompson, EC Carroll, RJ Ruppert, D

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Statistics in Medicine, 2022, 41, (7), pp. 1191-1204
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Cui, E
dc.contributor.author	Thompson, EC
dc.contributor.author	Carroll, RJ
dc.contributor.author	Ruppert, D
dc.date.accessioned	2022-12-02T01:11:00Z
dc.date.available	2021-10-29
dc.date.available	2022-12-02T01:11:00Z
dc.date.issued	2022
dc.identifier.citation	Statistics in Medicine, 2022, 41, (7), pp. 1191-1204
dc.identifier.issn	0277-6715
dc.identifier.issn	1097-0258
dc.identifier.uri	http://hdl.handle.net/10453/164033
dc.description.abstract	We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modeling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey and quantify nonlinear and interpretable shapes of score components for all-cause mortality.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Wiley
dc.relation	National Cancer InstituteU01CA057030
dc.relation.ispartof	Statistics in Medicine
dc.relation.isbasedon	10.1002/sim.9262
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics, 1117 Public Health and Health Services
dc.subject.classification	Statistics & Probability
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Exercise
dc.subject.mesh	Humans
dc.subject.mesh	Linear Models
dc.subject.mesh	Models, Statistical
dc.subject.mesh	Nutrition Surveys
dc.subject.mesh	Risk Factors
dc.subject.mesh	Humans
dc.subject.mesh	Exercise
dc.subject.mesh	Nutrition Surveys
dc.subject.mesh	Models, Statistical
dc.subject.mesh	Linear Models
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Risk Factors
dc.title	A semiparametric risk score for physical activity.
dc.type	Journal Article
utslib.citation.volume	41
utslib.location.activity	England
utslib.for	0104 Statistics
utslib.for	1117 Public Health and Health Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-02T01:10:59Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	41
utslib.citation.issue	7

Abstract:

We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modeling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey and quantify nonlinear and interpretable shapes of score components for all-cause mortality.

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http://hdl.handle.net/10453/164033