A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

Gazioglu, S; Wei, J; Jennings, EM; Carroll, RJ

A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data

Gazioglu, S Wei, J Jennings, EM Carroll, RJ

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Publication Type:: Journal Article
Citation:: Statistics in Biosciences, 2013, 5 (2), pp. 250 - 260
Issue Date:: 2013-11-01

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Field	Value	Language
dc.contributor.author	Gazioglu, S	en_US
dc.contributor.author	Wei, J	en_US
dc.contributor.author	Jennings, EM	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2013-11-01	en_US
dc.identifier.citation	Statistics in Biosciences, 2013, 5 (2), pp. 250 - 260	en_US
dc.identifier.issn	1867-1764	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118068
dc.description.abstract	Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y,X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology. © 2013 International Chinese Statistical Association.	en_US
dc.relation.ispartof	Statistics in Biosciences	en_US
dc.relation.isbasedon	10.1007/s12561-013-9094-9	en_US
dc.title	A Note on Penalized Regression Spline Estimation in the Secondary Analysis of Case-Control Data	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	5	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y,X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to the case-control sampling, and to avoid the biased sampling that arises from the design, it is typical to use the control data only. In this paper, we develop penalized regression spline methodology that uses all the data, and improves precision of estimation compared to using only the controls. A simulation study and an empirical example are used to illustrate the methodology. © 2013 International Chinese Statistical Association.

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http://hdl.handle.net/10453/118068