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Nonlinear, Multilevel Mixed-Effects Approach for Modeling Longitudinal Standard Automated Perimetry Data in Glaucoma
PURPOSE. Ordinary least squares linear regression (OLSLR) analyses are inappropriate for performing trend analysis on repeatedly measured longitudinal data. This study examines multilevel linear mixed-effects (LME) and nonlinear mixed-effects (NLME) methods to model longitudinally collected perimetr...
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| Hoofdauteurs: | , , |
|---|---|
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
The Association for Research in Vision and Ophthalmology
2013
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3747790/ https://ncbi.nlm.nih.gov/pubmed/23833069 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/iovs.13-12236 |
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