Skip to content

Opening book details…

Can I read Predictors of Refraction Prediction Error After Cataract Surgery: a Shared Parameter Model to Account for Missing Post-operative Measurements on EtoBox?

Predictors of Refraction Prediction Error After Cataract Surgery: a Shared Parameter Model to Account for Missing Post-operative Measurements by D. Claire Miller; Samantha MaWhinney; Jennifer L. Patnaik; Karen L. Christopher; Anne M. Lynch; Brandie D. Wagner is a Mathematics article available to read on EtoBox.

What is Predictors of Refraction Prediction Error After Cataract Surgery: a Shared Parameter Model to Account for Missing Post-operative Measurements about?

Cataract surgery is a common procedure that involves removing the cataractous lens of the eye and implanting a new clear plastic lens, with a goal of improving vision and often changing the refraction of the eye. Unexpected refractive outcomes, or refraction prediction error (PE), may be more likely to occur among patients with certain preexisting ocular conditions. However, longitudinal refractive measurements are often missing after surgery, making it difficult to accurately assess which ocular comorbidities lead to increased PE. Moreover, patients with ideal refractive outcomes in one or both eyes may be less likely to return to the clinic, thus more likely to have missing measurements that are missing not at random (MNAR). Despite this potential complication to data analysis, very few studies evaluating PE address the missing data mechanism and the effect it may have on the results. We propose the application of a shared parameter model to reduce bias in situations of MNAR data and compare this to a linear mixed model that is not able to account for this mechanism. We also conduct a simulation study to better understand the most plausible missing mechanism in our study and to c

Who reads Predictors of Refraction Prediction Error After Cataract Surgery: a Shared Parameter Model to Account for Missing Post-operative Measurements?

It is typically read by researchers, students, and practitioners in Mathematics.

Author
D. Claire Miller; Samantha MaWhinney; Jennifer L. Patnaik; Karen L. Christopher; Anne M. Lynch; Brandie D. Wagner
Publisher
Springer Science and Business Media LLC
Published
2021
Language
EN
Field
Mathematics (Physical Sciences)

More by D. Claire Miller; Samantha MaWhinney; Jennifer L. Patnaik; Karen L. Christopher; Anne M. Lynch; Brandie D. Wagner

Browse all works by D. Claire Miller; Samantha MaWhinney; Jennifer L. Patnaik; Karen L. Christopher; Anne M. Lynch; Brandie D. Wagner