期刊
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
卷 10, 期 12, 页码 -出版社
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/tvst.10.12.4
关键词
glaucoma; visual field; perimetry; hierarchical model; Bayesian
Developed hierarchical Bayesian models to account for the nature of visual field progression data, with censored models showing the smallest bias in rate-of-progression. Results indicate that Bayesian models performed better in terms of Hit-rate and time-to-progression compared to other methods. Account for censoring improved precision of estimates, with minimal effect from accounting for heteroskedasticity.
Purpose: To develop a Bayesian model (BM) for visual field (VF) progression accounting for the hierarchical, censored and heteroskedastic nature of the data. Methods: Three versions of a hierarchical BM were developed: a simple linear (Hi-linear); censored at 0 dB (Hi-censored); heteroskedastic censored (Hi-HSK). For the latter, we modeled the test variability according to VF sensitivity using a large test-retest cohort (1396 VFs, 146 eyes with glaucoma). We analyzed a large cohort of 44,371 VF tests from 3352 eyes from five glaucoma clinics. We quantified the bias in the estimated rate-of-progression, the detection of progression (Hit-rate [HR]), the median time-to progression and the prediction error of future observations (mean absolute error [MAE]). HR and time-to-progression were compared at matched false-positive-rate (FPR), quantified using permutations of a separate test-retest cohort (360 tests, 30 eyes with glaucoma). BMs were compared to simple linear regression and Permutation-Analyses of Pointwise-Linear-Regression. Differences in time-to-progression were tested using survival analysis. Results: Censored models showed the smallest bias in the rate-of-progression. The three BMs performed very similarly in terms of HR and time-to-progression and always better than the other methods. The average reduction in time-to-progression was 37% with the BMs (P < 0.001) at 5% FPR. MAE for prediction was very similar among methods. Conclusions: Bayesian hierarchical models improved the detection of VF progression. Accounting for censoring improves the precision of the estimates, but minimal effect is provided by accounting for heteroskedasticity. Translational Relevance: These results are relevant for quantification of VF progression in practice and for clinical trials.
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