Semiparametric regression analysis of interval-censored data

Goetghebeur, E; Ryan, L

Semiparametric regression analysis of interval-censored data

Goetghebeur, E Ryan, L

Permalink

Publication Type:: Journal Article
Citation:: Biometrics, 2000, 56 (4), pp. 1139 - 1144
Issue Date:: 2000-01-01

Closed Access

	Filename	Description	Size
	2012000371OK.pdf		1.14 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Goetghebeur, E	en_US
dc.contributor.author	Ryan, L https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	2000-01-01	en_US
dc.identifier.citation	Biometrics, 2000, 56 (4), pp. 1139 - 1144	en_US
dc.identifier.issn	0006-341X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26243
dc.description.abstract	We propose a semiparametric approach to the proportional hazards regression analysis of interval-censored data. An EM algorithm based on an approximate likelihood leads to an M-step that involves maximizing a standard Cox partial likelihood to estimate regression coefficients and then using the Breslow estimator for the unknown baseline hazards. The E-step takes a particularly simple form because all incomplete data appear as linear terms in the complete-data log likelihood. The algorithm of Turnbull (1976, Journal of the Royal Statistical Society, Series B 38, 290-295) is used to determine times at which the hazard can take positive mass. We found multiple imputation to yield an easily computed variance estimate that appears to be more reliable than asymptotic methods with small to moderately sized data sets. In the right-censored survival setting, the approach reduces to the standard Cox proportional hazards analysis, while the algorithm reduces to the one suggested by Clayton and Cuzick (1985, Applied Statistics 34, 148-156). The method is illustrated on data from the breast cancer cosmetics trial, previously analyzed by Finkelstein (1986, Biometrics 42, 845-854) and several subsequent authors.	en_US
dc.relation.ispartof	Biometrics	en_US
dc.relation.isbasedon	10.1111/j.0006-341X.2000.01139.x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Breast Neoplasms	en_US
dc.subject.mesh	Antineoplastic Agents	en_US
dc.subject.mesh	Radiotherapy	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Likelihood Functions	en_US
dc.subject.mesh	Proportional Hazards Models	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Biometry	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Female	en_US
dc.title	Semiparametric regression analysis of interval-censored data	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	56	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
dc.location.activity	Tsukuba, Japan
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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	56	en_US

Abstract:

We propose a semiparametric approach to the proportional hazards regression analysis of interval-censored data. An EM algorithm based on an approximate likelihood leads to an M-step that involves maximizing a standard Cox partial likelihood to estimate regression coefficients and then using the Breslow estimator for the unknown baseline hazards. The E-step takes a particularly simple form because all incomplete data appear as linear terms in the complete-data log likelihood. The algorithm of Turnbull (1976, Journal of the Royal Statistical Society, Series B 38, 290-295) is used to determine times at which the hazard can take positive mass. We found multiple imputation to yield an easily computed variance estimate that appears to be more reliable than asymptotic methods with small to moderately sized data sets. In the right-censored survival setting, the approach reduces to the standard Cox proportional hazards analysis, while the algorithm reduces to the one suggested by Clayton and Cuzick (1985, Applied Statistics 34, 148-156). The method is illustrated on data from the breast cancer cosmetics trial, previously analyzed by Finkelstein (1986, Biometrics 42, 845-854) and several subsequent authors.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26243