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Hierarchical Censored Bayesian Analysis of Visual Field Progression

Montesano, G., Garway-Heath, D. F., Ometto, G. and Crabb, D. P. ORCID: 0000-0001-8754-3902 (2021). Hierarchical Censored Bayesian Analysis of Visual Field Progression. Translational Vision Science & Technology, 10(12), 4. doi: 10.1167/tvst.10.12.4

Abstract

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.

Publication Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 International License.
Publisher Keywords: glaucoma; visual field; perimetry; hierarchical model; Bayesian
Subjects: R Medicine > RE Ophthalmology
Departments: School of Health Sciences > Optometry & Visual Science
Date available in CRO: 21 Oct 2021 08:01
Date deposited: 21 October 2021
Date of acceptance: 18 July 2021
Date of first online publication: 5 October 2021
URI: https://openaccess.city.ac.uk/id/eprint/26928
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