Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214504000001178
DC FieldValue
dc.titleUnbiased estimating equations from working correlation models for irregularly timed repeated measures
dc.contributor.authorWang, Y.-G.
dc.contributor.authorCarey, V.J.
dc.date.accessioned2014-10-28T05:16:23Z
dc.date.available2014-10-28T05:16:23Z
dc.date.issued2004-09
dc.identifier.citationWang, Y.-G., Carey, V.J. (2004-09). Unbiased estimating equations from working correlation models for irregularly timed repeated measures. Journal of the American Statistical Association 99 (467) : 845-853. ScholarBank@NUS Repository. https://doi.org/10.1198/016214504000001178
dc.identifier.issn01621459
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105450
dc.description.abstractThe method of generalized estimating equations (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation models for irregularly timed repeated measures to supplement and enhance GEE. The supplementary estimating equations are obtained by differentiation of the Cholesky decomposition of the working correlation, or as score equations for decoupled Gaussian pseudolikelihood. The estimating equations are solved with computational effort equivalent to that required for a first-order GEE. Full details and analytic expressions are developed for a generalized Markovian model that was evaluated through simulation. Large-sample "sandwich" standard errors for working correlation parameter estimates are derived and shown to have good performance. The proposed estimating functions are further illustrated in an analysis of repeated measures of pulmonary function in children.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1198/016214504000001178
dc.sourceScopus
dc.subjectBias
dc.subjectCorrelation
dc.subjectEfficiency
dc.subjectEstimating function
dc.subjectLongitudinal data
dc.subjectRepeated measure
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1198/016214504000001178
dc.description.sourcetitleJournal of the American Statistical Association
dc.description.volume99
dc.description.issue467
dc.description.page845-853
dc.identifier.isiut000223857500032
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.