Predictors of refraction prediction error after cataract surgery: a shared parameter model to account for missing post-operative measurements
Author:
Funder
Research to Prevent Blindness
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s10260-021-00570-w.pdf
Reference24 articles.
1. Albert PS (2019) Shared random parameter models: a legacy of the biostatistics program at the National Heart. Lung Blood Inst Stat Med 38:501–511. https://doi.org/10.1002/sim.8011
2. Bacchetti P (2013) Logarithmic Transformation. https://www.ctspedia.org/do/view/CTSpedia/LogTransformation
3. Brilleman SL et al (2019) Joint longitudinal and time-to-event models for multilevel hierarchical data. Stat Methods Med Res 28:3502–3515. https://doi.org/10.1177/0962280218808821
4. Bunce C, Quartilho A, Freemantle N, Doré CJ (2016) Ophthalmic statistics note 8: missing data—exploring the unknown. Br J Ophthalmol 100:291. https://doi.org/10.1136/bjophthalmol-2015-307821
5. Congdon N et al (2013) Assessment of cataract surgical outcomes in settings where follow-up is poor: PRECOG, a multicentre observational study. Lancet Global Health 1:e37–e45. https://doi.org/10.1016/S2214-109X(13)70003-2
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