Artificial Intelligence Glaucoma Progression Forecasting from Variable Visual Field Test-Retest Reliability: A Hierarchical Bayesian Evaluation of Healthcare Monitoring Intervals

Visual field test-retest variability in glaucoma care is not a nuisance parameter but a structured source of information about how often monitoring should occur. The framework is evaluated through a design-based power analysis of a before-after-control-impact estimator under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect at 80% power falls as cluster count rises. The contribution is a formalisation of reliability-adaptive monitoring intervals as a decision problem, together with a demonstration that the before-after-control-impact design can recover the relevant interaction at feasible cluster counts. The framework is proposed as a methodological scaffold for prospective evaluation rather than as a report of clinical outcomes.