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Yuri Fujino, Hiroshi Murata, Chihiro Mayama, Ryo Asaoka; Applying “Lasso” Regression to Predict Future Visual Field Progression in Glaucoma Patients. Invest. Ophthalmol. Vis. Sci. 2015;56(4):2334-2339. doi: 10.1167/iovs.15-16445.
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© ARVO (1962-2015); The Authors (2016-present)
We evaluated the usefulness of various regression models, including least absolute shrinkage and selection operator (Lasso) regression, to predict future visual field (VF) progression in glaucoma patients.
Series of 10 VFs (Humphrey Field Analyzer 24-2 SITA-standard) from each of 513 eyes in 324 open-angle glaucoma patients, obtained in 4.9 ± 1.3 years (mean ± SD), were investigated. For each patient, the mean of all total deviation values (mTD) in the 10th VF was predicted using varying numbers of prior VFs (ranging from the first three VFs to all previous VFs) by applying ordinary least squares linear regression (OLSLR), M-estimator robust regression (M-robust), MM-estimator robust regression (MM-robust), skipped regression (Skipped), deepest regression (Deepest), and Lasso regression. Absolute prediction errors then were compared.
With OLSLR, prediction error varied between 5.7 ± 6.1 (using the first three VFs) and 1.2 ± 1.1 dB (using all nine previous VFs). Prediction accuracy was not significantly improved with M-robust, MM-robust, Skipped, or Deepest regression in almost all VF series; however, a significantly smaller prediction error was obtained with Lasso regression even with a small number of VFs (using first 3 VFs, 2.0 ± 2.2; using all nine previous VFs, 1.2 ± 1.1 dB).
Prediction errors using OLSLR are large when only a small number of VFs are included in the regression. Lasso regression offers much more accurate predictions, especially in short VF series.
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