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Article: Nonlinear mixed-effects modeling of MNREAD data

TitleNonlinear mixed-effects modeling of MNREAD data
Authors
Issue Date2008
PublisherAssociation for Research in Vision and Ophthalmology. The Journal's web site is located at http://www.iovs.org
Citation
Investigative Ophthalmology And Visual Science, 2008, v. 49 n. 2, p. 828-835 How to Cite?
AbstractPurpose. It is often difficult to estimate parameters from individual clinical data because of noisy or incomplete measurements. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME by analyzing data from the MNREAD, a continuous-text reading-acuity chart. Methods. The authors analyzed MNREAD data (measurements of reading speed vs. print size) for two groups: 42 adult observers with normal vision and 14 patients with age-related macular degeneration (AMD). Truncated sets of MNREAD data were generated from the individual observers with normal vision. The MNREAD data were fitted with a two-limb function and an exponential-decay function using an individual curve-fitting approach and an NLME modeling approach. Results. The exponential-decay function provided slightly better fits than the two-limb function. When the parameter estimates from the truncated data sets were used to predict the missing data, NLME modeling gave better predictions than individual fitting. NLME modeling gave reasonable parameter estimates for AMD patients even when individual fitting returned unrealistic estimates. Conclusions. These analyses showed that (1) an exponential-decay function fits MNREAD data very well, (2) NLME modeling provides a statistical framework for analyzing MNREAD data, and (3) NLME analysis provides a way of estimating MNREAD parameters even for incomplete data sets. The present results demonstrate the potential value of NLME modeling for clinical vision data. copyright© Association for Research in Vision and Ophthalmology.
Persistent Identifierhttp://hdl.handle.net/10722/89502
ISSN
2015 Impact Factor: 3.427
2015 SCImago Journal Rankings: 2.008
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCheung, SHen_HK
dc.contributor.authorKallie, CSen_HK
dc.contributor.authorLegge, GEen_HK
dc.contributor.authorCheong, AMYen_HK
dc.date.accessioned2010-09-06T09:57:51Z-
dc.date.available2010-09-06T09:57:51Z-
dc.date.issued2008en_HK
dc.identifier.citationInvestigative Ophthalmology And Visual Science, 2008, v. 49 n. 2, p. 828-835en_HK
dc.identifier.issn0146-0404en_HK
dc.identifier.urihttp://hdl.handle.net/10722/89502-
dc.description.abstractPurpose. It is often difficult to estimate parameters from individual clinical data because of noisy or incomplete measurements. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME by analyzing data from the MNREAD, a continuous-text reading-acuity chart. Methods. The authors analyzed MNREAD data (measurements of reading speed vs. print size) for two groups: 42 adult observers with normal vision and 14 patients with age-related macular degeneration (AMD). Truncated sets of MNREAD data were generated from the individual observers with normal vision. The MNREAD data were fitted with a two-limb function and an exponential-decay function using an individual curve-fitting approach and an NLME modeling approach. Results. The exponential-decay function provided slightly better fits than the two-limb function. When the parameter estimates from the truncated data sets were used to predict the missing data, NLME modeling gave better predictions than individual fitting. NLME modeling gave reasonable parameter estimates for AMD patients even when individual fitting returned unrealistic estimates. Conclusions. These analyses showed that (1) an exponential-decay function fits MNREAD data very well, (2) NLME modeling provides a statistical framework for analyzing MNREAD data, and (3) NLME analysis provides a way of estimating MNREAD parameters even for incomplete data sets. The present results demonstrate the potential value of NLME modeling for clinical vision data. copyright© Association for Research in Vision and Ophthalmology.en_HK
dc.languageengen_HK
dc.publisherAssociation for Research in Vision and Ophthalmology. The Journal's web site is located at http://www.iovs.orgen_HK
dc.relation.ispartofInvestigative Ophthalmology and Visual Scienceen_HK
dc.titleNonlinear mixed-effects modeling of MNREAD dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0146-0404&volume=49&issue=2&spage=828&epage=835&date=2008&atitle=Nonlinear+mixed-effects+modeling+of+MNREAD+dataen_HK
dc.identifier.emailCheung, SH:singhang@hku.hken_HK
dc.identifier.authorityCheung, SH=rp00590en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1167/iovs.07-0555en_HK
dc.identifier.pmid18235034en_HK
dc.identifier.scopuseid_2-s2.0-40649114781en_HK
dc.identifier.hkuros140569en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-40649114781&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume49en_HK
dc.identifier.issue2en_HK
dc.identifier.spage828en_HK
dc.identifier.epage835en_HK
dc.identifier.eissn1552-5783-
dc.identifier.isiWOS:000252747100047-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCheung, SH=7202473508en_HK
dc.identifier.scopusauthoridKallie, CS=14048421100en_HK
dc.identifier.scopusauthoridLegge, GE=7005064208en_HK
dc.identifier.scopusauthoridCheong, AMY=7003847650en_HK
dc.identifier.citeulike3838903-

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