Please use this identifier to cite or link to this item:
https://doi.org/10.1198/016214504000001178
DC Field | Value | |
---|---|---|
dc.title | Unbiased estimating equations from working correlation models for irregularly timed repeated measures | |
dc.contributor.author | Wang, Y.-G. | |
dc.contributor.author | Carey, V.J. | |
dc.date.accessioned | 2014-10-28T05:16:23Z | |
dc.date.available | 2014-10-28T05:16:23Z | |
dc.date.issued | 2004-09 | |
dc.identifier.citation | Wang, 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.issn | 01621459 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/105450 | |
dc.description.abstract | The 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.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1198/016214504000001178 | |
dc.source | Scopus | |
dc.subject | Bias | |
dc.subject | Correlation | |
dc.subject | Efficiency | |
dc.subject | Estimating function | |
dc.subject | Longitudinal data | |
dc.subject | Repeated measure | |
dc.type | Article | |
dc.contributor.department | STATISTICS & APPLIED PROBABILITY | |
dc.description.doi | 10.1198/016214504000001178 | |
dc.description.sourcetitle | Journal of the American Statistical Association | |
dc.description.volume | 99 | |
dc.description.issue | 467 | |
dc.description.page | 845-853 | |
dc.identifier.isiut | 000223857500032 | |
Appears in Collections: | Staff Publications |
Show simple item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.